Research ArticleA New Approach to Diagnose Parkinsonrsquos Disease Usinga Structural Cooccurrence Matrix for a Similarity Analysis

JoatildeoW M de Souza Shara S A Alves Elizacircngela de S RebouccedilasJefferson S Almeida and Pedro P Rebouccedilas Filho

Laboratorio de Processamento de Imagens Sinais e Computacao Aplicada (LAPISCO) Instituto Federal de EducacaoCiencia e Tecnologia do Ceara Fortaleza CE Brazil

Correspondence should be addressed to Pedro P Reboucas Filho pedrosarfifceedubr

Received 1 January 2018 Accepted 14 March 2018 Published 24 April 2018

Academic Editor Roshan J Martis

Copyright copy 2018 Joao W M de Souza et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Parkinsonrsquos disease affectsmillions of people around the world and consequently various approaches have emerged to help diagnosethis disease amongwhichwe can highlight handwriting exams Extracting features fromhandwriting exams is an important contri-bution of the computational field for the diagnosis of this disease In this paper we propose an approach thatmeasures the similaritybetween the exam template and the handwritten trace of the patient following the exam template This similarity was measuredusing the Structural Cooccurrence Matrix to calculate how close the handwritten trace of the patient is to the exam template Theproposed approach was evaluated using various exam templates and the handwritten traces of the patient Each of these variationswas used together with the Naıve Bayes OPF and SVM classifiers In conclusion the proposed approach was proven to be betterthan the existing methods found in the literature and is therefore a promising tool for the diagnosis of Parkinsonrsquos disease

1 Introduction

According to the World Health Organization neurologicaldisorders such as Parkinsonrsquos disease multiple sclerosisAlzheimerrsquos disease epilepsy shingles and stroke are nervoussystem diseases that affect the brain the spine and the nervesthat connect them Approximately 16 in 60 people sufferfrom some neurological disease [1] Parkinsonrsquos disease (PD)first described by Parkinson [2] is a degenerative diseaseof the central nervous system associated with a chronicand progressive movement disorder [1] Parkinsonrsquos DiseaseFoundation claims that this disease affects about 7ndash10millionpeople worldwide and 4 of people with PD are diagnosedbefore the age of 50 The cause is unknown and there is nocure for PD but an early diagnosis helps in the treatment thatcontinues throughout the patientrsquos life

PD studies in the computational field aremainly focusedon diagnosing the disease The literature shows that someworks aim to recognize the presence or absence of PDand identify the patients degree of severity [3 4] andanother extracts features fromhandwriting exams [5] among

others [6ndash12] Most of the studies use signals from examsto make a diagnosis However studies related to a diagnosisthrough handwriting exams (handwriting exams based onthe quality of the patientrsquos tracing results can be used for PDdiagnosis) are quite scarce [5]

Handwriting exams may be conducted on paper [13] orby using more sophisticated methods such as digitizers [5]or even a smartphone [14] This type of exam has advantagesas it is easily obtainable and can also provide diversity suchas spirals ellipses connected syllables connected words andmany other ways to test a patientrsquos ability to trace such forms[15ndash19]However the extraction of the features is complicatedsince the paper exams have some printing error and theinformation in this type of exam is not so clear

This paper compares handwriting templates and patientshandwriting using a novel Structural Cooccurrence Matrix-based approach which relies on similarity metrics asattributesThis approach was used because feature extractionthrough cooccurrence between similar images appeared asa promising method for this application The proposal was

HindawiComputational Intelligence and NeuroscienceVolume 2018 Article ID 7613282 8 pageshttpsdoiorg10115520187613282

2 Computational Intelligence and Neuroscience

(a) CG individual (b) PG individual

(c) CG individual (d) PG individualFigure 1 (a-b) Handwriting exams in a spiral format (c-d) handwriting exams in a meander format [13]

evaluated using three classifiers and the results were com-pared against those in [13]

The rest of this paper is organized as follows The essen-tials of handwriting exams and somemachine learningmeth-ods are explained in Sections 2 and 3 respectively Section 4describes the Structural Cooccurrence Matrix features Ourproposal is presented in Section 5 The experimental setup ispresented in Section 6 Then the results and discussion aregiven in Section 7 and finally the conclusions are given inSection 8

2 Diagnosis of Parkinsonrsquos Disease throughHandwriting Exams

There are several examples in the literature that apply hand-writing exams to diagnose PD Drotar et al recorded theexamination time as a parameter for PD diagnosis [5] whileSurangsrirat used a polar coordinates interpretation to definethe features [20]There are also works based on the differencebetween the patientrsquos trace and the template [13]

Pereira et al [13] introduced a new method to obtain theexam which is performed on paper and relies on underliningthe template correctly Pereira et al also proposed a set of

images composed of handwriting exams known as HandPDdataset

The HandPD dataset [13] consists of 736 images fromhandwriting exams divided into two groups the ControlGroup (CG) containing 144 images and the Patient Group(PG) containing 592 images The exams were obtained from92 individuals in which 18 were healthy individuals (CG)and 74 were patients (PG) diagnosed with PD These examswere performed at the Botucatu Medical School of theState University of Sao Paulo Brazil and include spiral andmeander handwriting exam templates Figure 1 shows somesamples from this dataset [13]

Pereira et al [13] used an approach to define the datasetattributes based on the differences between the exam template(ET) and the handwritten trace (HT) Pereira et al [13]described the HandPD dataset exams using the 9 attributeslisted below

(1) Root mean square (RMS) of the difference betweenHT and ET radius

RMS = radic 1119899119899sum119894=0

(119903119894HT minus 119903119894ET)2 (1)

Computational Intelligence and Neuroscience 3

where 119899 is the number of sample points drawn foreach HT and ET skeleton and 119903119894HT and 119903119894ET denote theHT and ET radius which is basically the length ofthe straight line that connects the 119894th sampled pointrespectively to the center of the spiral or meander

(2) Maximum difference between HT and ET radius

Δmax = argmax119894

10038161003816100381610038161003816119903119894HT minus 119903119894ET10038161003816100381610038161003816 (2)

(3) Minimum difference between HT and ET radius

Δmin = argmin119894

10038161003816100381610038161003816119903119894HT minus 119903119894ET10038161003816100381610038161003816 (3)

(4) Another attribute is standard deviation of the differ-ence between HT and ET radius

(5) Mean relative tremor (MRT) [13] is a quantitativeevaluation to measure the ldquoamount of tremorrdquo of agiven individualrsquos HT

MRT = 1119899 minus 119889119899sum119894=119889

10038161003816100381610038161003816119903119894ET minus 119903119894minus119889+1ET10038161003816100381610038161003816 (4)

where 119889 is the displacement of the sample points usedto compute the radius difference

(6) There is maximum ET radius(7) Another attribute is minimum ET radius(8) There is also standard deviation of HT radius(9) The last one is the number of times the difference

between HT and ET radius changes from negative topositive or the opposite

3 Overview of Machine Learning Methods

Pereira et al [13] evaluated their approach using experi-ments which involved theNaıve Bayes Optimum-Path Forest(OPF) and Support Vector Machines (SVM) classifiersTherefore we conducted our experiments with the sameaforementioned classifiers in order to compare our proposalwith Pereira et alrsquos

All three classifiers deal with recognition problems indifferent ways Based on Bayesrsquo Theorem the Naıve Bayesclassifier is a probabilistic approach that makes a strongindependence assumption among the predictors [21] Thecommon terms in such a theorem a priori and a posterioriare related to an indication of known probabilities and theprobability in future indications respectively

On the other hand the OPF classifier designs the recog-nition problem based on the Graph Theory in a particularfeature space [22] A competition process is establishedamong some patterns called prototypes that are determinedduring the training step and each connected pattern carriesits cost After that the optimal-path forest is computed usingthe Image Forest Transform (ITF) algorithm The LibOPFlibrary [23] was used to implement the OPF classifier usedin this work

Input signal

f f

SCM

PQ

Q

k k(f)

Two-dimensionalhistogram

Attributes(3 groups)

Figure 2 An example of an SCM [25]

The SVM classifier is based on the Vapnik statisticallearning theory [24] The main goal of this classifier is to findan optimal hyperplane able to separate the patterns of eachlabel This optimal hyperplane is obtained through the linearseparation of the patterns in space and after the feature spaceis defined the SVM determines the optimal hyperplane

4 Structural Cooccurrence Matrix (SCM)

In this section we present a brief description of the SCMwhich is the basis for the approach of this article as proposedby Bezerra Ramalho et al [25]

Bezerra Ramalho et al [25] introduced a general purposestructural image analytical method based on cooccurrencestatistics saved in a matrix namely Structural CooccurrenceMatrix (SCM) This method analyzes in an 119899-dimensionalspace the relation between low-level structures of two dis-crete signals Figure 2 shows an example of this method

This method has a variable 119896 which is any invariantimage filter that exposes saliences of the image 119891 Here 119896is configurable for each application Thus depending on thea priori knowledge of the characteristics of the image beinganalyzed 119896 can be either a high-pass filter or a low-pass filter[25]

5 Proposed Approach

In this section we present our SCM-based approach to diag-nose PDThis new approach extracts features from the spiraland meander handwriting exams of the HandPD dataset[13] Figure 3 shows the proposed flowchart This flowchartpresents one of the combinations which uses handwrittentrace (c) and exam template (b)

The first step is the exam segmentation which returns twonew images the exam template (ET) and the handwrittentrace (HT) This step is presented in detail in Figure 4 Theimages are obtained by applying digital image processingtechniques on the handwriting exams

In order to obtain the ET segmentation the image issmoothed through a Median filter (5 times 5) to eliminate thenoise picked up during the acquisition of the exam Then anerosion (9 times 9 ellipse structure) is applied to ensure thatthere is no discontinuity in the ET segmentation After thatan empirically defined threshold is used Finally an erosion

4 Computational Intelligence and Neuroscience

Proposed approach(a)

(b)(c) Examsegmentation

Grayscale

Input images

Handwritten trace Exam template

Two-dimensionalhistogram SCM

(i) COR(ii) IDM(iii) ENT(iv) CSD

(v) CSR(vi) MDR

(viii) CAD

g

p

f

Attributes(vii) D＋

Figure 3 Flowchart of the proposed approach

Segmentation process

Handwrittentrace

Examtemplate

Differenceoperation

Colorthreshold

Otsuthreshold

Median filter5 times 5

Grayscale

Input image

ErodeErode

(a)

(b)

Figure 4 An example of the segmentation process (a) segmentation of exam template (b) segmentation of handwritten trace

Computational Intelligence and Neuroscience 5

is applied again with the same structuring element to obtainthe real size of the ET as shown in Figure 4

In order to obtain the HT segmentation the image is alsosmoothed through the Median filter (5 times 5) to eliminatethe noise picked up during the examsThen the handwritingexam is converted to the grayscale after which the Otsuthreshold is applied Finally we apply a difference operationbetween the grayscale image and the ET These steps areshown in Figure 4

The second step in our proposal converts the segmentedexams to the grayscale for the next step The third stepis feature extraction from the images obtained after thesegmentation and conversion to the grayscale These imagesare used as the input to SCM as showed in Figure 2 Thefeature extraction through the SCM is a method to analyzethe relationship between signals in this case in a two-dimensional space

A slight modification was made to the original SCMmethod The SCM has two input parameters an image anda filter We replaced the filter with another image The newconfiguration of the input parameters is presented as anexample in Figure 3This proposal is intended to enhance thedifferences between the two input images by computing thesimilarity approximation between the patientrsquos trace and theexam templateThere is no need to configure a filter as in thisSCMmethod the filter was replaced for another image

Three combinations of three images were proposed as theSCM inputs which were then used for a complete analysis(i) handwriting exam and handwritten trace (ii) handwritingexam and exam template and (iii) handwritten trace andexam template

The scalar attributes obtained through the SCMmethodare computed by the SCM generated between the inputimages These attributes are divided into three groups sta-tistical group information group and divergent group [25]All these attributes are computed based on the SCM matrixrepresented by M = m119894119895 and some are related to themarginal distribution P of the SCM A brief description ofeach attribute is given below

(i) Correlation (COR) measures how the information iscorrelated The COR is given by

COR = 119873minus1sum119894119895=0

m119894119895(119894 minus 120583119894) (119895 minus 120583119895)radic12059011989421205901198952 isin [minus1 1] (5)

where 120583119894 and 120583119895 are the average value of rows andcolumns of M 120590119894 and 120590119895 are the standard deviationof rows and columns of M

(ii) Inverse difference moment (IDM) measures thehomogeneity and it is given by

IDM = 119873minus1sum119894119895=0

m1198941198951 + (1003816100381610038161003816119894 minus 1198951003816100381610038161003816) isin [0 1] (6)

(iii) Entropy (ENT) the randomness of the information ismeasured and it is given by

ENT = minus119873minus1sum119894119895=0

m119894119895 log (m119894119895) (7)

(iv) Chi-square distance (CSD) since the P0119894 = m119894119895 = 119895and P119890119894 = sum119873minus1119895=0 m119894119895 gt= 119894

CSD = 119873minus1sum119894=0

(P0119894 minus P119890119894 )2P119890119894 (8)

(v) Chi-square distance ratio (CSR) with the distribu-tions of the quadrants I PI

119894 = sum1198732minus1119894=0 sum1198732minus1119895=0 m119894119895 andIII PIII119894 = sum119873minus1119894=1198732sum119873minus1119895=1198732m119894119895 the CSR is given by

CSR = 119873minus1sum119894=0

(PI119894 minus P119898119894 )2P119898119894 (9)

where P119898119894 = (PI119894 + PIII119894 )2

(vi) Mean absolute difference ratio (MDR) calculatesthe statistical dispersion where MDmin =min(MD119901119888 MD119901119903) MDmax = max(MD119901119888 MD119901119903)and MD119901 = sum119873minus1119894=0 sum119873minus1119895=0 P(119878119894)P(119878119895)|119878119894 minus 119878119895| TheMDRis given by

MDR = MDminMDmax (10)

where 119878119894 119878119895 119894 = 0 119873 minus 1 are the indexes of thenonzero values of the marginal distribution P

(vii) Divergence of Kullback Leibler (119863KL) measures theinformation gain between bordering distributionsand it is given by

119863KL = 119873minus1sum119894=0

log(P119888119894P119903119894)P119888119894 (11)

where P119888119894 P119903119894 = 0 where P119903119894 = 0(viii) Complementary absolute difference (CAD) compares

the two probability distributions P119888119894 and P119903119894 The CAD

is given by

CAD = 1 minus 119873minus1sum119894=0

1003816100381610038161003816P119888119894 minus P119903119894 1003816100381610038161003816 (12)

At the end of the SCMprocess we have the attributes to beapplied as pattern recognition inputs to anymachine learningmethod

6 Computational Intelligence and Neuroscience

Table 1 Results from the best classifiers and combinations

Meander Spiral MSAcc () Acc () Acc ()

BayesahArr c 7277 plusmn 991 6957 plusmn 1107 6595 plusmn 205ahArr b 7511 plusmn 399 7636 plusmn 426 6838 plusmn 218chArr b 7772 plusmn 427 8201 plusmn 553 7139 plusmn 239

OPFahArr c 7554 plusmn 376 6973 plusmn 363 6428 plusmn 205ahArr b 6859 plusmn 376 7049 plusmn 363 6113 plusmn 257chArr b 7750 plusmn 275 7571 plusmn 439 6721 plusmn 198

SVMahArr c 8005 plusmn 180 7804 plusmn 274 6763 plusmn 273ahArr b 7500 plusmn 290 7804 plusmn 285 6530 plusmn 292chArr b 8223 plusmn 302 8554 plusmn 362 7413 plusmn 227

6 Experimental Setup

We conducted some experiments with the handwritingexams used by Pereira et al [13] These exams concern thepatientrsquos ability to underline the two exam templates spiraland meander First the spiral and meander exam attributeswere extracted by the proposed approach presented in Sec-tion 5 Then we applied three machine learning methodsNaıve Bayes OPF and SVM in 3 different experiments (i)considering only the spiral exam attributes (ii) consideringonly themeander exam attributes and (iii) considering spiraland meander exam attributes together That is we evaluatethe proposed approach using the two handwriting formatsseparately and also together After that we compared theresults with those of Pereira et al [13]

The number of HandPD dataset samples used in all ofthe experiments are divided into 368 spiral exam samplesand 368meander exam samples resulting in 736 samplesWeemphasize that each subset is divided into 296 samples fromthe CG and 72 samples from the PG

All the three experiments were conducted with 75 forthe training set 25 for testing set and the remaining 25 forthe validation set We applied a cross-validation with 20 runsfor the reliability of the resultsThe setup of the OPF classifierwas configuredwith the Euclidean distance and the SVMwiththe radial basis function The SVM and OPF classifiers wereautomatically optimised by choosing the optimal parametersfrom each method Parameters are considered optimal whenthe cross-validation estimates the minimal error [26ndash32]

Accuracy was used to classify the machine learningmethods This metric is defined using the terms obtained inthe confusion matrix generated after applying the machinelearning methods and refers to the closeness of a measuredvalue to a standard or known value

7 Results and Discussion

In this section we provide an analysis of the results usingthe handwriting exams from Pereira et al [13] These exams

present the patientrsquos ability to underline the exam templateThe results were obtained by applying machine learningmethods to the features extracted from cooccurrence matrixobtained over the SCM extractor used in this work

Table 1 presents the classification results of the combi-nations between the images obtained in the segmentation(ET and HT) and the handwritten exam using SCM wherethe meander and spiral formats were evaluated separatelyand together The handwritten exam exam template andhandwritten trace are represented in Table 1 as a b and crespectively

Table 1 also shows that the combination of the meanderand spiral exam obtained the lowest accuracy rates The verysignificant difference between the spiral andmeander formatsjustifies these low rates

Another important factor observed in the best resultsobtained with the classifiers in Table 1 was the predominanceof the best results using the combination of the handwrittentrace and the exam template which proves that these two arethe most similar

The results of this work were compared with the resultsobtained byPereira et al approach [13] inTable 2which showseach classifier with subdivisions for the experiments usinghandwriting exams in the formats of meanders spirals andthe combination of the meanders and spirals Immediatelyafter that there is a new subdivision between the resultsobtained with our proposal and those of Pereira et al [13]In Table 2 the highest accuracy results are in underline fontand the lowest ones in italic font for each of the classifiersAlso highlight in bold are the highest and the lowest accuracyresults of both approaches and experiments

The results obtained in this paper proved to be superiorin all of the experiments in comparison to those of Pereiraproposal [13] The highest accuracy was 8554 using theapproach proposed This highest result was provided withthe SVM classifier using the handwriting in a spiral formatwith the combination of the handwritten trace and theexam template The lowest accuracy among the best resultspresented in Table 2 was 4579

Computational Intelligence and Neuroscience 7

Table 2 Comparison between the best results of this paper and the best results of the Pereira et al approach [13]

Classif Database Feature extractor Accuracy ()

Bayes

Meander Pereira et al approach [13] 5920 plusmn 478Proposed approach 7772 plusmn 427

Spiral Pereira et al approach [13] 6423 plusmn 711Proposed approach 8201 plusmn 553

MS Pereira et al approach [13] 4579 plusmn 415Proposed approach 7139 plusmn 239

OPF

Meander Pereira et al approach [13] 5754 plusmn 635Proposed approach 7750 plusmn 275

Spiral Pereira et al approach [13] 5248 plusmn 532Proposed approach 7571 plusmn 439

MS Pereira et al approach [13] 5586 plusmn 363Proposed approach 6721 plusmn 198

SVM

Meander Pereira et al approach [13] 6672 plusmn 533Proposed approach 8223 plusmn 302

Spiral Pereira et al approach [13] 5016 plusmn 171Proposed approach 8554 plusmn 362

MS Pereira et al approach [13] 5861 plusmn 284Proposed approach 7413 plusmn 227

8 Conclusion

The proposal of this paper is based on the patientrsquos trace andthe exam template using the SCM method for a similarityapproximation The SVM classifier with the RBF kernel andhandwriting in the spiral format proved being the mostpromising for this application This configuration had anaccuracy of 8554 for the combination of the handwrittentrace and the exam template in the feature extraction Theresults obtained in this paperwere 2131superior to the bestresult achieved by Pereira et al approach [13]

We conclude that this is promising approach to help in thediagnosis of PD Another advantage of this proposal is thatthere is no need to configure a filter to obtain the structuredcooccurrence matrix facilitating its application

These results encourage us to propose future works forhandwriting feature extraction

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

Pedro P Reboucas Filho acknowledges the sponsorship fromthe IFCE via Grant PROINFRA PPG2017 Joao W Mde Souza acknowledges the CNPq for financial supportJefferson S Almeida acknowledges the FUNCAP for financialsupport

References

[1] WHO Neurological Disorders Public Health Challenges WorldHealth Organization 2006

[2] J Parkinson ldquoAn essay on the shaking palsy 1817rdquo The Journalof Neuropsychiatry and Clinical Neurosciences vol 14 no 2 pp223ndash236 2002

[3] J Chen Z Lei L Liu G Zhao and M Pietikainen ldquoRoLoDRobust local descriptors for computer visionrdquoNeurocomputingvol 184 pp 1-2 2016

[4] N Bouadjenek H Nemmour and Y Chibani ldquoRobust soft-biometrics prediction from off-line handwriting analysisrdquoApplied Soft Computing vol 46 pp 980ndash990 2016

[5] P Drotar J Mekyska I Rektorova L Masarova Z SmekalandM Faundez-Zanuy ldquoEvaluation of handwriting kinematicsand pressure for differential diagnosis of Parkinsonrsquos diseaserdquoArtificial Intelligence in Medicine vol 67 pp 39ndash46 2016

[6] M S Baby A J Saji and C S Kumar ldquoParkinsons diseaseclassification using wavelet transform based feature extractionof gait datardquo in Proceedings of the 2017 IEEE InternationalConference on Circuit Power and Computing TechnologiesICCPCT 2017 India April 2017

[7] A Brahim L Khedher J M Gorriz et al ldquoA proposedcomputer-aided diagnosis system for Parkinsonrsquos disease classi-fication using 123I-FP-CIT imagingrdquo in Proceedings of the 3rdInternational Conference on Advanced Technologies for Signaland Image Processing ATSIP 2017 Morocco May 2017

[8] C R Pereira S A T Weber C Hook G H Rosa and J PPapa ldquoDeep learning-aided Parkinsonrsquos disease diagnosis fromhandwritten dynamicsrdquo in Proceedings of the 29th SIBGRAPIConference on Graphics Patterns and Images SIBGRAPI 2016pp 340ndash346 Brazil October 2016

[9] V V Shah S Goyal and H J Palanthandalam-Madapusi ldquoAperspective on the use of high-frequency stimulation in deepbrain stimulation for Parkinsonrsquos diseaserdquo in Proceedings of the2nd Indian Control Conference ICC 2016 pp 19ndash24 IndiaJanuary 2016

[10] N Zhi B K Jaeger A Gouldstone R Sipahi and S FrankldquoToward Monitoring Parkinsonrsquos Through Analysis of StaticHandwriting Samples A Quantitative Analytical Frameworkrdquo

8 Computational Intelligence and Neuroscience

IEEE Journal of Biomedical and Health Informatics vol 21 no2 pp 488ndash495 2017

[11] A-M Schiffer A J Nevado-Holgado A Johnen A RSchonberger G R Fink and R I Schubotz ldquoIntact actionsegmentation in Parkinsonrsquos disease Hypothesis testing usinga novel computational approachrdquoNeuropsychologia vol 78 pp29ndash40 2015

[12] R Guzman-Cabrera M Gomez-Sarabia M Torres-CisnerosM A Escobar-Acevedo and J R Guzman-Sepulveda ldquoParkin-sonrsquos disease Improved diagnosis using image processingrdquo inProceedings of the 2017 Photonics North (PN) pp 1-1 OttawaON Canada June 2017

[13] C R Pereira D R Pereira F A Silva et al ldquoA new computervision-based approach to aid the diagnosis of Parkinsonrsquosdiseaserdquo Computer Methods and Programs in Biomedicine vol136 pp 79ndash88 2016

[14] R Graca R S E Castro and J Cevada ldquoParkdetect Earlydiagnosing parkinsonrsquos diseaserdquo in Proceedings of the 9th IEEEInternational Symposium on Medical Measurements and Appli-cations IEEE MeMeA 2014 Portugal June 2014

[15] C R Pereira D R Pereira F A D Silva et al ldquoA steptowards the automated diagnosis of parkinsonrsquos disease Ana-lyzing handwritingmovementsrdquo in Proceedings of the 28th IEEEInternational Symposium on Computer-Based Medical SystemsCBMS 2015 pp 171ndash176 Brazil June 2015

[16] P Drotar J Mekyska I Rektorova L Masarova Z Smekal andM Faundez-Zanuy ldquoAnalysis of in-air movement in handwrit-ing A novel marker for Parkinsonrsquos diseaserdquo ComputerMethodsand Programs in Biomedicine vol 117 no 3 pp 405ndash411 2014

[17] P Drotar J Mekyska Z Smekal I Rektorova L MasarovaandM Faundez-Zanuy ldquoContribution of different handwritingmodalities to differential diagnosis of Parkinsonrsquos Diseaserdquoin Proceedings of the 2015 IEEE International Symposium onMedical Measurements and Applications MeMeA 2015 pp 344ndash348 Italy May 2015

[18] C Taleb M Khachab C Mokbel and L Likforman-SulemldquoFeature selection for an improved Parkinsonrsquos disease identi-fication based on handwritingrdquo in Proceedings of the 2017 1stInternational Workshop on Arabic Script Analysis and Recogni-tion (ASAR) pp 52ndash56 Nancy France April 2017

[19] Z Smekal J Mekyska I Rektorova and M Faundez-ZanuyldquoAnalysis of neurological disorders based on digital processingof speech and handwritten textrdquo in Proceedings of the 2013International Symposium on Signals Circuits and Systems ISSCS2013 Romania July 2013

[20] D Surangsrirat A Intarapanich C Thanawattano R Bhi-dayasiri S Petchrutchatachart and C Anan ldquoTremor assess-ment using spiral analysis in time-frequency domainrdquo inProceedings of the IEEE SoutheastCon 2013MovingAmerica intothe Future USA April 2013

[21] STheodoridis andK Koutroumbas Pattern Recognition fourthedition Edition Academic Press fourth edition Edition 2009

[22] J P Papa A X Falcao and C T N Suzuki ldquoSupervised patternclassification based on optimum-path forestrdquo InternationalJournal of Imaging Systems and Technology vol 19 no 2 pp120ndash131 2009

[23] J P Papa C T N Suzuki and A X Falcao A library forthe design of optimum-path forest classifiers 2014 httpwwwicunicampbrafalcaolibopfindexhtmlLibopf

[24] V N Vapnik Statistical Learning Theory John Wiley Sons Inc2009

[25] G L Bezerra Ramalho D S Ferreira P P Reboucas Filhoand F N S de Medeiros ldquoRotation-invariant feature extractionusing a structural co-occurrence matrixrdquoMeasurement vol 94pp 406ndash415 2016

[26] S L Gomes E D S Reboucas E C Neto et al ldquoEmbeddedreal-time speed limit sign recognition using image processingand machine learning techniquesrdquo Neural Computing andApplications vol 28 pp 573ndash584 2017

[27] P P R Filho A C D S Barros G L B Ramalho et alldquoAutomated recognition of lung diseases in CT images basedon the optimum-path forest classifierrdquo Neural Computing andApplications pp 1ndash14 2017

[28] P P Reboucas Filho E D S Reboucas L B Marinho R MSarmento J M R S Tavares and V H C de AlbuquerqueldquoAnalysis of human tissue densities A new approach to extractfeatures from medical imagesrdquo Pattern Recognition Letters vol94 pp 211ndash218 2017

[29] P P Reboucas Filho R M Sarmento G B Holanda and D deAlencar Lima ldquoNew approach to detect and classify stroke inskull CT images via analysis of brain tissue densitiesrdquoComputerMethods and Programs in Biomedicine vol 148 pp 27ndash43 2017

[30] E C Neto S L Gomes P P Reboucas Filho and V H CDe Albuquerque ldquoBrazilian vehicle identification using a newembedded plate recognition systemrdquoMeasurement vol 70 pp36ndash46 2015

[31] L B Marinho J S Almeida J W M Souza V H CAlbuquerque and P P Reboucas Filho ldquoA novel mobile robotlocalization approach based on topological maps using classi-fication with reject option in omnidirectional imagesrdquo ExpertSystems with Applications vol 72 pp 1ndash17 2017

[32] P P Reboucas Filho J C D Santos F N C Freitas et alldquoNew approach to evaluate a non-grain oriented electricalsteel electromagnetic performance using photomicrographicanalysis via digital image processingrdquo Journal of MaterialsResearch and Technology 2017

Computational Intelligence and Neuroscience 3

where 119899 is the number of sample points drawn foreach HT and ET skeleton and 119903119894HT and 119903119894ET denote theHT and ET radius which is basically the length ofthe straight line that connects the 119894th sampled pointrespectively to the center of the spiral or meander

(2) Maximum difference between HT and ET radius

Δmax = argmax119894

10038161003816100381610038161003816119903119894HT minus 119903119894ET10038161003816100381610038161003816 (2)

(3) Minimum difference between HT and ET radius

Δmin = argmin119894

10038161003816100381610038161003816119903119894HT minus 119903119894ET10038161003816100381610038161003816 (3)

(4) Another attribute is standard deviation of the differ-ence between HT and ET radius

(5) Mean relative tremor (MRT) [13] is a quantitativeevaluation to measure the ldquoamount of tremorrdquo of agiven individualrsquos HT

MRT = 1119899 minus 119889119899sum119894=119889

10038161003816100381610038161003816119903119894ET minus 119903119894minus119889+1ET10038161003816100381610038161003816 (4)

where 119889 is the displacement of the sample points usedto compute the radius difference

(6) There is maximum ET radius(7) Another attribute is minimum ET radius(8) There is also standard deviation of HT radius(9) The last one is the number of times the difference

between HT and ET radius changes from negative topositive or the opposite

3 Overview of Machine Learning Methods

Pereira et al [13] evaluated their approach using experi-ments which involved theNaıve Bayes Optimum-Path Forest(OPF) and Support Vector Machines (SVM) classifiersTherefore we conducted our experiments with the sameaforementioned classifiers in order to compare our proposalwith Pereira et alrsquos

All three classifiers deal with recognition problems indifferent ways Based on Bayesrsquo Theorem the Naıve Bayesclassifier is a probabilistic approach that makes a strongindependence assumption among the predictors [21] Thecommon terms in such a theorem a priori and a posterioriare related to an indication of known probabilities and theprobability in future indications respectively

On the other hand the OPF classifier designs the recog-nition problem based on the Graph Theory in a particularfeature space [22] A competition process is establishedamong some patterns called prototypes that are determinedduring the training step and each connected pattern carriesits cost After that the optimal-path forest is computed usingthe Image Forest Transform (ITF) algorithm The LibOPFlibrary [23] was used to implement the OPF classifier usedin this work

Input signal

f f

SCM

PQ

Q

k k(f)

Two-dimensionalhistogram

Attributes(3 groups)

Figure 2 An example of an SCM [25]

The SVM classifier is based on the Vapnik statisticallearning theory [24] The main goal of this classifier is to findan optimal hyperplane able to separate the patterns of eachlabel This optimal hyperplane is obtained through the linearseparation of the patterns in space and after the feature spaceis defined the SVM determines the optimal hyperplane

4 Structural Cooccurrence Matrix (SCM)

In this section we present a brief description of the SCMwhich is the basis for the approach of this article as proposedby Bezerra Ramalho et al [25]

Bezerra Ramalho et al [25] introduced a general purposestructural image analytical method based on cooccurrencestatistics saved in a matrix namely Structural CooccurrenceMatrix (SCM) This method analyzes in an 119899-dimensionalspace the relation between low-level structures of two dis-crete signals Figure 2 shows an example of this method

This method has a variable 119896 which is any invariantimage filter that exposes saliences of the image 119891 Here 119896is configurable for each application Thus depending on thea priori knowledge of the characteristics of the image beinganalyzed 119896 can be either a high-pass filter or a low-pass filter[25]

5 Proposed Approach

In this section we present our SCM-based approach to diag-nose PDThis new approach extracts features from the spiraland meander handwriting exams of the HandPD dataset[13] Figure 3 shows the proposed flowchart This flowchartpresents one of the combinations which uses handwrittentrace (c) and exam template (b)

The first step is the exam segmentation which returns twonew images the exam template (ET) and the handwrittentrace (HT) This step is presented in detail in Figure 4 Theimages are obtained by applying digital image processingtechniques on the handwriting exams

In order to obtain the ET segmentation the image issmoothed through a Median filter (5 times 5) to eliminate thenoise picked up during the acquisition of the exam Then anerosion (9 times 9 ellipse structure) is applied to ensure thatthere is no discontinuity in the ET segmentation After thatan empirically defined threshold is used Finally an erosion

4 Computational Intelligence and Neuroscience

Proposed approach(a)

(b)(c) Examsegmentation

Grayscale

Input images

Handwritten trace Exam template

Two-dimensionalhistogram SCM

(i) COR(ii) IDM(iii) ENT(iv) CSD

(v) CSR(vi) MDR

(viii) CAD

g

p

f

Attributes(vii) D＋

Figure 3 Flowchart of the proposed approach

Segmentation process

Handwrittentrace

Examtemplate

Differenceoperation

Colorthreshold

Otsuthreshold

Median filter5 times 5

Grayscale

Input image

ErodeErode

(a)

(b)

Figure 4 An example of the segmentation process (a) segmentation of exam template (b) segmentation of handwritten trace

Computational Intelligence and Neuroscience 5

is applied again with the same structuring element to obtainthe real size of the ET as shown in Figure 4

In order to obtain the HT segmentation the image is alsosmoothed through the Median filter (5 times 5) to eliminatethe noise picked up during the examsThen the handwritingexam is converted to the grayscale after which the Otsuthreshold is applied Finally we apply a difference operationbetween the grayscale image and the ET These steps areshown in Figure 4

The second step in our proposal converts the segmentedexams to the grayscale for the next step The third stepis feature extraction from the images obtained after thesegmentation and conversion to the grayscale These imagesare used as the input to SCM as showed in Figure 2 Thefeature extraction through the SCM is a method to analyzethe relationship between signals in this case in a two-dimensional space

A slight modification was made to the original SCMmethod The SCM has two input parameters an image anda filter We replaced the filter with another image The newconfiguration of the input parameters is presented as anexample in Figure 3This proposal is intended to enhance thedifferences between the two input images by computing thesimilarity approximation between the patientrsquos trace and theexam templateThere is no need to configure a filter as in thisSCMmethod the filter was replaced for another image

Three combinations of three images were proposed as theSCM inputs which were then used for a complete analysis(i) handwriting exam and handwritten trace (ii) handwritingexam and exam template and (iii) handwritten trace andexam template

The scalar attributes obtained through the SCMmethodare computed by the SCM generated between the inputimages These attributes are divided into three groups sta-tistical group information group and divergent group [25]All these attributes are computed based on the SCM matrixrepresented by M = m119894119895 and some are related to themarginal distribution P of the SCM A brief description ofeach attribute is given below

(i) Correlation (COR) measures how the information iscorrelated The COR is given by

COR = 119873minus1sum119894119895=0

m119894119895(119894 minus 120583119894) (119895 minus 120583119895)radic12059011989421205901198952 isin [minus1 1] (5)

where 120583119894 and 120583119895 are the average value of rows andcolumns of M 120590119894 and 120590119895 are the standard deviationof rows and columns of M

(ii) Inverse difference moment (IDM) measures thehomogeneity and it is given by

IDM = 119873minus1sum119894119895=0

m1198941198951 + (1003816100381610038161003816119894 minus 1198951003816100381610038161003816) isin [0 1] (6)

(iii) Entropy (ENT) the randomness of the information ismeasured and it is given by

ENT = minus119873minus1sum119894119895=0

m119894119895 log (m119894119895) (7)

(iv) Chi-square distance (CSD) since the P0119894 = m119894119895 = 119895and P119890119894 = sum119873minus1119895=0 m119894119895 gt= 119894

CSD = 119873minus1sum119894=0

(P0119894 minus P119890119894 )2P119890119894 (8)

(v) Chi-square distance ratio (CSR) with the distribu-tions of the quadrants I PI

119894 = sum1198732minus1119894=0 sum1198732minus1119895=0 m119894119895 andIII PIII119894 = sum119873minus1119894=1198732sum119873minus1119895=1198732m119894119895 the CSR is given by

CSR = 119873minus1sum119894=0

(PI119894 minus P119898119894 )2P119898119894 (9)

where P119898119894 = (PI119894 + PIII119894 )2

(vi) Mean absolute difference ratio (MDR) calculatesthe statistical dispersion where MDmin =min(MD119901119888 MD119901119903) MDmax = max(MD119901119888 MD119901119903)and MD119901 = sum119873minus1119894=0 sum119873minus1119895=0 P(119878119894)P(119878119895)|119878119894 minus 119878119895| TheMDRis given by

MDR = MDminMDmax (10)

where 119878119894 119878119895 119894 = 0 119873 minus 1 are the indexes of thenonzero values of the marginal distribution P

(vii) Divergence of Kullback Leibler (119863KL) measures theinformation gain between bordering distributionsand it is given by

119863KL = 119873minus1sum119894=0

log(P119888119894P119903119894)P119888119894 (11)

where P119888119894 P119903119894 = 0 where P119903119894 = 0(viii) Complementary absolute difference (CAD) compares

the two probability distributions P119888119894 and P119903119894 The CAD

is given by

CAD = 1 minus 119873minus1sum119894=0

1003816100381610038161003816P119888119894 minus P119903119894 1003816100381610038161003816 (12)

At the end of the SCMprocess we have the attributes to beapplied as pattern recognition inputs to anymachine learningmethod

6 Computational Intelligence and Neuroscience

Table 1 Results from the best classifiers and combinations

Meander Spiral MSAcc () Acc () Acc ()

BayesahArr c 7277 plusmn 991 6957 plusmn 1107 6595 plusmn 205ahArr b 7511 plusmn 399 7636 plusmn 426 6838 plusmn 218chArr b 7772 plusmn 427 8201 plusmn 553 7139 plusmn 239

OPFahArr c 7554 plusmn 376 6973 plusmn 363 6428 plusmn 205ahArr b 6859 plusmn 376 7049 plusmn 363 6113 plusmn 257chArr b 7750 plusmn 275 7571 plusmn 439 6721 plusmn 198

SVMahArr c 8005 plusmn 180 7804 plusmn 274 6763 plusmn 273ahArr b 7500 plusmn 290 7804 plusmn 285 6530 plusmn 292chArr b 8223 plusmn 302 8554 plusmn 362 7413 plusmn 227

6 Experimental Setup

We conducted some experiments with the handwritingexams used by Pereira et al [13] These exams concern thepatientrsquos ability to underline the two exam templates spiraland meander First the spiral and meander exam attributeswere extracted by the proposed approach presented in Sec-tion 5 Then we applied three machine learning methodsNaıve Bayes OPF and SVM in 3 different experiments (i)considering only the spiral exam attributes (ii) consideringonly themeander exam attributes and (iii) considering spiraland meander exam attributes together That is we evaluatethe proposed approach using the two handwriting formatsseparately and also together After that we compared theresults with those of Pereira et al [13]

The number of HandPD dataset samples used in all ofthe experiments are divided into 368 spiral exam samplesand 368meander exam samples resulting in 736 samplesWeemphasize that each subset is divided into 296 samples fromthe CG and 72 samples from the PG

All the three experiments were conducted with 75 forthe training set 25 for testing set and the remaining 25 forthe validation set We applied a cross-validation with 20 runsfor the reliability of the resultsThe setup of the OPF classifierwas configuredwith the Euclidean distance and the SVMwiththe radial basis function The SVM and OPF classifiers wereautomatically optimised by choosing the optimal parametersfrom each method Parameters are considered optimal whenthe cross-validation estimates the minimal error [26ndash32]

Accuracy was used to classify the machine learningmethods This metric is defined using the terms obtained inthe confusion matrix generated after applying the machinelearning methods and refers to the closeness of a measuredvalue to a standard or known value

7 Results and Discussion

In this section we provide an analysis of the results usingthe handwriting exams from Pereira et al [13] These exams

present the patientrsquos ability to underline the exam templateThe results were obtained by applying machine learningmethods to the features extracted from cooccurrence matrixobtained over the SCM extractor used in this work

Table 1 presents the classification results of the combi-nations between the images obtained in the segmentation(ET and HT) and the handwritten exam using SCM wherethe meander and spiral formats were evaluated separatelyand together The handwritten exam exam template andhandwritten trace are represented in Table 1 as a b and crespectively

Table 1 also shows that the combination of the meanderand spiral exam obtained the lowest accuracy rates The verysignificant difference between the spiral andmeander formatsjustifies these low rates

Another important factor observed in the best resultsobtained with the classifiers in Table 1 was the predominanceof the best results using the combination of the handwrittentrace and the exam template which proves that these two arethe most similar

The results of this work were compared with the resultsobtained byPereira et al approach [13] inTable 2which showseach classifier with subdivisions for the experiments usinghandwriting exams in the formats of meanders spirals andthe combination of the meanders and spirals Immediatelyafter that there is a new subdivision between the resultsobtained with our proposal and those of Pereira et al [13]In Table 2 the highest accuracy results are in underline fontand the lowest ones in italic font for each of the classifiersAlso highlight in bold are the highest and the lowest accuracyresults of both approaches and experiments

The results obtained in this paper proved to be superiorin all of the experiments in comparison to those of Pereiraproposal [13] The highest accuracy was 8554 using theapproach proposed This highest result was provided withthe SVM classifier using the handwriting in a spiral formatwith the combination of the handwritten trace and theexam template The lowest accuracy among the best resultspresented in Table 2 was 4579

Computational Intelligence and Neuroscience 7

Table 2 Comparison between the best results of this paper and the best results of the Pereira et al approach [13]

Classif Database Feature extractor Accuracy ()

Bayes

Meander Pereira et al approach [13] 5920 plusmn 478Proposed approach 7772 plusmn 427

Spiral Pereira et al approach [13] 6423 plusmn 711Proposed approach 8201 plusmn 553

MS Pereira et al approach [13] 4579 plusmn 415Proposed approach 7139 plusmn 239

OPF

Meander Pereira et al approach [13] 5754 plusmn 635Proposed approach 7750 plusmn 275

Spiral Pereira et al approach [13] 5248 plusmn 532Proposed approach 7571 plusmn 439

MS Pereira et al approach [13] 5586 plusmn 363Proposed approach 6721 plusmn 198

SVM

Meander Pereira et al approach [13] 6672 plusmn 533Proposed approach 8223 plusmn 302

Spiral Pereira et al approach [13] 5016 plusmn 171Proposed approach 8554 plusmn 362

MS Pereira et al approach [13] 5861 plusmn 284Proposed approach 7413 plusmn 227

8 Conclusion

The proposal of this paper is based on the patientrsquos trace andthe exam template using the SCM method for a similarityapproximation The SVM classifier with the RBF kernel andhandwriting in the spiral format proved being the mostpromising for this application This configuration had anaccuracy of 8554 for the combination of the handwrittentrace and the exam template in the feature extraction Theresults obtained in this paperwere 2131superior to the bestresult achieved by Pereira et al approach [13]

We conclude that this is promising approach to help in thediagnosis of PD Another advantage of this proposal is thatthere is no need to configure a filter to obtain the structuredcooccurrence matrix facilitating its application

These results encourage us to propose future works forhandwriting feature extraction

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

Pedro P Reboucas Filho acknowledges the sponsorship fromthe IFCE via Grant PROINFRA PPG2017 Joao W Mde Souza acknowledges the CNPq for financial supportJefferson S Almeida acknowledges the FUNCAP for financialsupport

References

[1] WHO Neurological Disorders Public Health Challenges WorldHealth Organization 2006

[2] J Parkinson ldquoAn essay on the shaking palsy 1817rdquo The Journalof Neuropsychiatry and Clinical Neurosciences vol 14 no 2 pp223ndash236 2002

[3] J Chen Z Lei L Liu G Zhao and M Pietikainen ldquoRoLoDRobust local descriptors for computer visionrdquoNeurocomputingvol 184 pp 1-2 2016

[4] N Bouadjenek H Nemmour and Y Chibani ldquoRobust soft-biometrics prediction from off-line handwriting analysisrdquoApplied Soft Computing vol 46 pp 980ndash990 2016

[5] P Drotar J Mekyska I Rektorova L Masarova Z SmekalandM Faundez-Zanuy ldquoEvaluation of handwriting kinematicsand pressure for differential diagnosis of Parkinsonrsquos diseaserdquoArtificial Intelligence in Medicine vol 67 pp 39ndash46 2016

[6] M S Baby A J Saji and C S Kumar ldquoParkinsons diseaseclassification using wavelet transform based feature extractionof gait datardquo in Proceedings of the 2017 IEEE InternationalConference on Circuit Power and Computing TechnologiesICCPCT 2017 India April 2017

[7] A Brahim L Khedher J M Gorriz et al ldquoA proposedcomputer-aided diagnosis system for Parkinsonrsquos disease classi-fication using 123I-FP-CIT imagingrdquo in Proceedings of the 3rdInternational Conference on Advanced Technologies for Signaland Image Processing ATSIP 2017 Morocco May 2017

[8] C R Pereira S A T Weber C Hook G H Rosa and J PPapa ldquoDeep learning-aided Parkinsonrsquos disease diagnosis fromhandwritten dynamicsrdquo in Proceedings of the 29th SIBGRAPIConference on Graphics Patterns and Images SIBGRAPI 2016pp 340ndash346 Brazil October 2016

[9] V V Shah S Goyal and H J Palanthandalam-Madapusi ldquoAperspective on the use of high-frequency stimulation in deepbrain stimulation for Parkinsonrsquos diseaserdquo in Proceedings of the2nd Indian Control Conference ICC 2016 pp 19ndash24 IndiaJanuary 2016

[10] N Zhi B K Jaeger A Gouldstone R Sipahi and S FrankldquoToward Monitoring Parkinsonrsquos Through Analysis of StaticHandwriting Samples A Quantitative Analytical Frameworkrdquo

8 Computational Intelligence and Neuroscience

IEEE Journal of Biomedical and Health Informatics vol 21 no2 pp 488ndash495 2017

[11] A-M Schiffer A J Nevado-Holgado A Johnen A RSchonberger G R Fink and R I Schubotz ldquoIntact actionsegmentation in Parkinsonrsquos disease Hypothesis testing usinga novel computational approachrdquoNeuropsychologia vol 78 pp29ndash40 2015

[12] R Guzman-Cabrera M Gomez-Sarabia M Torres-CisnerosM A Escobar-Acevedo and J R Guzman-Sepulveda ldquoParkin-sonrsquos disease Improved diagnosis using image processingrdquo inProceedings of the 2017 Photonics North (PN) pp 1-1 OttawaON Canada June 2017

[13] C R Pereira D R Pereira F A Silva et al ldquoA new computervision-based approach to aid the diagnosis of Parkinsonrsquosdiseaserdquo Computer Methods and Programs in Biomedicine vol136 pp 79ndash88 2016

[14] R Graca R S E Castro and J Cevada ldquoParkdetect Earlydiagnosing parkinsonrsquos diseaserdquo in Proceedings of the 9th IEEEInternational Symposium on Medical Measurements and Appli-cations IEEE MeMeA 2014 Portugal June 2014

[15] C R Pereira D R Pereira F A D Silva et al ldquoA steptowards the automated diagnosis of parkinsonrsquos disease Ana-lyzing handwritingmovementsrdquo in Proceedings of the 28th IEEEInternational Symposium on Computer-Based Medical SystemsCBMS 2015 pp 171ndash176 Brazil June 2015

[16] P Drotar J Mekyska I Rektorova L Masarova Z Smekal andM Faundez-Zanuy ldquoAnalysis of in-air movement in handwrit-ing A novel marker for Parkinsonrsquos diseaserdquo ComputerMethodsand Programs in Biomedicine vol 117 no 3 pp 405ndash411 2014

[17] P Drotar J Mekyska Z Smekal I Rektorova L MasarovaandM Faundez-Zanuy ldquoContribution of different handwritingmodalities to differential diagnosis of Parkinsonrsquos Diseaserdquoin Proceedings of the 2015 IEEE International Symposium onMedical Measurements and Applications MeMeA 2015 pp 344ndash348 Italy May 2015

[18] C Taleb M Khachab C Mokbel and L Likforman-SulemldquoFeature selection for an improved Parkinsonrsquos disease identi-fication based on handwritingrdquo in Proceedings of the 2017 1stInternational Workshop on Arabic Script Analysis and Recogni-tion (ASAR) pp 52ndash56 Nancy France April 2017

[19] Z Smekal J Mekyska I Rektorova and M Faundez-ZanuyldquoAnalysis of neurological disorders based on digital processingof speech and handwritten textrdquo in Proceedings of the 2013International Symposium on Signals Circuits and Systems ISSCS2013 Romania July 2013

[20] D Surangsrirat A Intarapanich C Thanawattano R Bhi-dayasiri S Petchrutchatachart and C Anan ldquoTremor assess-ment using spiral analysis in time-frequency domainrdquo inProceedings of the IEEE SoutheastCon 2013MovingAmerica intothe Future USA April 2013

[21] STheodoridis andK Koutroumbas Pattern Recognition fourthedition Edition Academic Press fourth edition Edition 2009

[22] J P Papa A X Falcao and C T N Suzuki ldquoSupervised patternclassification based on optimum-path forestrdquo InternationalJournal of Imaging Systems and Technology vol 19 no 2 pp120ndash131 2009

[23] J P Papa C T N Suzuki and A X Falcao A library forthe design of optimum-path forest classifiers 2014 httpwwwicunicampbrafalcaolibopfindexhtmlLibopf

[24] V N Vapnik Statistical Learning Theory John Wiley Sons Inc2009

[25] G L Bezerra Ramalho D S Ferreira P P Reboucas Filhoand F N S de Medeiros ldquoRotation-invariant feature extractionusing a structural co-occurrence matrixrdquoMeasurement vol 94pp 406ndash415 2016

[26] S L Gomes E D S Reboucas E C Neto et al ldquoEmbeddedreal-time speed limit sign recognition using image processingand machine learning techniquesrdquo Neural Computing andApplications vol 28 pp 573ndash584 2017

[27] P P R Filho A C D S Barros G L B Ramalho et alldquoAutomated recognition of lung diseases in CT images basedon the optimum-path forest classifierrdquo Neural Computing andApplications pp 1ndash14 2017

[28] P P Reboucas Filho E D S Reboucas L B Marinho R MSarmento J M R S Tavares and V H C de AlbuquerqueldquoAnalysis of human tissue densities A new approach to extractfeatures from medical imagesrdquo Pattern Recognition Letters vol94 pp 211ndash218 2017

[29] P P Reboucas Filho R M Sarmento G B Holanda and D deAlencar Lima ldquoNew approach to detect and classify stroke inskull CT images via analysis of brain tissue densitiesrdquoComputerMethods and Programs in Biomedicine vol 148 pp 27ndash43 2017

[30] E C Neto S L Gomes P P Reboucas Filho and V H CDe Albuquerque ldquoBrazilian vehicle identification using a newembedded plate recognition systemrdquoMeasurement vol 70 pp36ndash46 2015

[31] L B Marinho J S Almeida J W M Souza V H CAlbuquerque and P P Reboucas Filho ldquoA novel mobile robotlocalization approach based on topological maps using classi-fication with reject option in omnidirectional imagesrdquo ExpertSystems with Applications vol 72 pp 1ndash17 2017

[32] P P Reboucas Filho J C D Santos F N C Freitas et alldquoNew approach to evaluate a non-grain oriented electricalsteel electromagnetic performance using photomicrographicanalysis via digital image processingrdquo Journal of MaterialsResearch and Technology 2017

4 Computational Intelligence and Neuroscience

Proposed approach(a)

(b)(c) Examsegmentation

Grayscale

Input images

Handwritten trace Exam template

Two-dimensionalhistogram SCM

(i) COR(ii) IDM(iii) ENT(iv) CSD

(v) CSR(vi) MDR

(viii) CAD

g

p

f

Attributes(vii) D＋

Figure 3 Flowchart of the proposed approach

Segmentation process

Handwrittentrace

Examtemplate

Differenceoperation

Colorthreshold

Otsuthreshold

Median filter5 times 5

Grayscale

Input image

ErodeErode

(a)

(b)

Figure 4 An example of the segmentation process (a) segmentation of exam template (b) segmentation of handwritten trace

Computational Intelligence and Neuroscience 5

is applied again with the same structuring element to obtainthe real size of the ET as shown in Figure 4

In order to obtain the HT segmentation the image is alsosmoothed through the Median filter (5 times 5) to eliminatethe noise picked up during the examsThen the handwritingexam is converted to the grayscale after which the Otsuthreshold is applied Finally we apply a difference operationbetween the grayscale image and the ET These steps areshown in Figure 4

The second step in our proposal converts the segmentedexams to the grayscale for the next step The third stepis feature extraction from the images obtained after thesegmentation and conversion to the grayscale These imagesare used as the input to SCM as showed in Figure 2 Thefeature extraction through the SCM is a method to analyzethe relationship between signals in this case in a two-dimensional space

A slight modification was made to the original SCMmethod The SCM has two input parameters an image anda filter We replaced the filter with another image The newconfiguration of the input parameters is presented as anexample in Figure 3This proposal is intended to enhance thedifferences between the two input images by computing thesimilarity approximation between the patientrsquos trace and theexam templateThere is no need to configure a filter as in thisSCMmethod the filter was replaced for another image

Three combinations of three images were proposed as theSCM inputs which were then used for a complete analysis(i) handwriting exam and handwritten trace (ii) handwritingexam and exam template and (iii) handwritten trace andexam template

The scalar attributes obtained through the SCMmethodare computed by the SCM generated between the inputimages These attributes are divided into three groups sta-tistical group information group and divergent group [25]All these attributes are computed based on the SCM matrixrepresented by M = m119894119895 and some are related to themarginal distribution P of the SCM A brief description ofeach attribute is given below

(i) Correlation (COR) measures how the information iscorrelated The COR is given by

COR = 119873minus1sum119894119895=0

m119894119895(119894 minus 120583119894) (119895 minus 120583119895)radic12059011989421205901198952 isin [minus1 1] (5)

where 120583119894 and 120583119895 are the average value of rows andcolumns of M 120590119894 and 120590119895 are the standard deviationof rows and columns of M

(ii) Inverse difference moment (IDM) measures thehomogeneity and it is given by

IDM = 119873minus1sum119894119895=0

m1198941198951 + (1003816100381610038161003816119894 minus 1198951003816100381610038161003816) isin [0 1] (6)

(iii) Entropy (ENT) the randomness of the information ismeasured and it is given by

ENT = minus119873minus1sum119894119895=0

m119894119895 log (m119894119895) (7)

(iv) Chi-square distance (CSD) since the P0119894 = m119894119895 = 119895and P119890119894 = sum119873minus1119895=0 m119894119895 gt= 119894

CSD = 119873minus1sum119894=0

(P0119894 minus P119890119894 )2P119890119894 (8)

(v) Chi-square distance ratio (CSR) with the distribu-tions of the quadrants I PI

119894 = sum1198732minus1119894=0 sum1198732minus1119895=0 m119894119895 andIII PIII119894 = sum119873minus1119894=1198732sum119873minus1119895=1198732m119894119895 the CSR is given by

CSR = 119873minus1sum119894=0

(PI119894 minus P119898119894 )2P119898119894 (9)

where P119898119894 = (PI119894 + PIII119894 )2

(vi) Mean absolute difference ratio (MDR) calculatesthe statistical dispersion where MDmin =min(MD119901119888 MD119901119903) MDmax = max(MD119901119888 MD119901119903)and MD119901 = sum119873minus1119894=0 sum119873minus1119895=0 P(119878119894)P(119878119895)|119878119894 minus 119878119895| TheMDRis given by

MDR = MDminMDmax (10)

where 119878119894 119878119895 119894 = 0 119873 minus 1 are the indexes of thenonzero values of the marginal distribution P

(vii) Divergence of Kullback Leibler (119863KL) measures theinformation gain between bordering distributionsand it is given by

119863KL = 119873minus1sum119894=0

log(P119888119894P119903119894)P119888119894 (11)

where P119888119894 P119903119894 = 0 where P119903119894 = 0(viii) Complementary absolute difference (CAD) compares

the two probability distributions P119888119894 and P119903119894 The CAD

is given by

CAD = 1 minus 119873minus1sum119894=0

1003816100381610038161003816P119888119894 minus P119903119894 1003816100381610038161003816 (12)

At the end of the SCMprocess we have the attributes to beapplied as pattern recognition inputs to anymachine learningmethod

6 Computational Intelligence and Neuroscience

Table 1 Results from the best classifiers and combinations

Meander Spiral MSAcc () Acc () Acc ()

BayesahArr c 7277 plusmn 991 6957 plusmn 1107 6595 plusmn 205ahArr b 7511 plusmn 399 7636 plusmn 426 6838 plusmn 218chArr b 7772 plusmn 427 8201 plusmn 553 7139 plusmn 239

OPFahArr c 7554 plusmn 376 6973 plusmn 363 6428 plusmn 205ahArr b 6859 plusmn 376 7049 plusmn 363 6113 plusmn 257chArr b 7750 plusmn 275 7571 plusmn 439 6721 plusmn 198

SVMahArr c 8005 plusmn 180 7804 plusmn 274 6763 plusmn 273ahArr b 7500 plusmn 290 7804 plusmn 285 6530 plusmn 292chArr b 8223 plusmn 302 8554 plusmn 362 7413 plusmn 227

6 Experimental Setup

We conducted some experiments with the handwritingexams used by Pereira et al [13] These exams concern thepatientrsquos ability to underline the two exam templates spiraland meander First the spiral and meander exam attributeswere extracted by the proposed approach presented in Sec-tion 5 Then we applied three machine learning methodsNaıve Bayes OPF and SVM in 3 different experiments (i)considering only the spiral exam attributes (ii) consideringonly themeander exam attributes and (iii) considering spiraland meander exam attributes together That is we evaluatethe proposed approach using the two handwriting formatsseparately and also together After that we compared theresults with those of Pereira et al [13]

The number of HandPD dataset samples used in all ofthe experiments are divided into 368 spiral exam samplesand 368meander exam samples resulting in 736 samplesWeemphasize that each subset is divided into 296 samples fromthe CG and 72 samples from the PG

All the three experiments were conducted with 75 forthe training set 25 for testing set and the remaining 25 forthe validation set We applied a cross-validation with 20 runsfor the reliability of the resultsThe setup of the OPF classifierwas configuredwith the Euclidean distance and the SVMwiththe radial basis function The SVM and OPF classifiers wereautomatically optimised by choosing the optimal parametersfrom each method Parameters are considered optimal whenthe cross-validation estimates the minimal error [26ndash32]

Accuracy was used to classify the machine learningmethods This metric is defined using the terms obtained inthe confusion matrix generated after applying the machinelearning methods and refers to the closeness of a measuredvalue to a standard or known value

7 Results and Discussion

In this section we provide an analysis of the results usingthe handwriting exams from Pereira et al [13] These exams

present the patientrsquos ability to underline the exam templateThe results were obtained by applying machine learningmethods to the features extracted from cooccurrence matrixobtained over the SCM extractor used in this work

Table 1 presents the classification results of the combi-nations between the images obtained in the segmentation(ET and HT) and the handwritten exam using SCM wherethe meander and spiral formats were evaluated separatelyand together The handwritten exam exam template andhandwritten trace are represented in Table 1 as a b and crespectively

Table 1 also shows that the combination of the meanderand spiral exam obtained the lowest accuracy rates The verysignificant difference between the spiral andmeander formatsjustifies these low rates

Another important factor observed in the best resultsobtained with the classifiers in Table 1 was the predominanceof the best results using the combination of the handwrittentrace and the exam template which proves that these two arethe most similar

The results of this work were compared with the resultsobtained byPereira et al approach [13] inTable 2which showseach classifier with subdivisions for the experiments usinghandwriting exams in the formats of meanders spirals andthe combination of the meanders and spirals Immediatelyafter that there is a new subdivision between the resultsobtained with our proposal and those of Pereira et al [13]In Table 2 the highest accuracy results are in underline fontand the lowest ones in italic font for each of the classifiersAlso highlight in bold are the highest and the lowest accuracyresults of both approaches and experiments

The results obtained in this paper proved to be superiorin all of the experiments in comparison to those of Pereiraproposal [13] The highest accuracy was 8554 using theapproach proposed This highest result was provided withthe SVM classifier using the handwriting in a spiral formatwith the combination of the handwritten trace and theexam template The lowest accuracy among the best resultspresented in Table 2 was 4579

Computational Intelligence and Neuroscience 7

Table 2 Comparison between the best results of this paper and the best results of the Pereira et al approach [13]

Classif Database Feature extractor Accuracy ()

Bayes

Meander Pereira et al approach [13] 5920 plusmn 478Proposed approach 7772 plusmn 427

Spiral Pereira et al approach [13] 6423 plusmn 711Proposed approach 8201 plusmn 553

MS Pereira et al approach [13] 4579 plusmn 415Proposed approach 7139 plusmn 239

OPF

Meander Pereira et al approach [13] 5754 plusmn 635Proposed approach 7750 plusmn 275

Spiral Pereira et al approach [13] 5248 plusmn 532Proposed approach 7571 plusmn 439

MS Pereira et al approach [13] 5586 plusmn 363Proposed approach 6721 plusmn 198

SVM

Meander Pereira et al approach [13] 6672 plusmn 533Proposed approach 8223 plusmn 302

Spiral Pereira et al approach [13] 5016 plusmn 171Proposed approach 8554 plusmn 362

MS Pereira et al approach [13] 5861 plusmn 284Proposed approach 7413 plusmn 227

8 Conclusion

The proposal of this paper is based on the patientrsquos trace andthe exam template using the SCM method for a similarityapproximation The SVM classifier with the RBF kernel andhandwriting in the spiral format proved being the mostpromising for this application This configuration had anaccuracy of 8554 for the combination of the handwrittentrace and the exam template in the feature extraction Theresults obtained in this paperwere 2131superior to the bestresult achieved by Pereira et al approach [13]

We conclude that this is promising approach to help in thediagnosis of PD Another advantage of this proposal is thatthere is no need to configure a filter to obtain the structuredcooccurrence matrix facilitating its application

These results encourage us to propose future works forhandwriting feature extraction

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

Pedro P Reboucas Filho acknowledges the sponsorship fromthe IFCE via Grant PROINFRA PPG2017 Joao W Mde Souza acknowledges the CNPq for financial supportJefferson S Almeida acknowledges the FUNCAP for financialsupport

References

[1] WHO Neurological Disorders Public Health Challenges WorldHealth Organization 2006

[2] J Parkinson ldquoAn essay on the shaking palsy 1817rdquo The Journalof Neuropsychiatry and Clinical Neurosciences vol 14 no 2 pp223ndash236 2002

[3] J Chen Z Lei L Liu G Zhao and M Pietikainen ldquoRoLoDRobust local descriptors for computer visionrdquoNeurocomputingvol 184 pp 1-2 2016

[4] N Bouadjenek H Nemmour and Y Chibani ldquoRobust soft-biometrics prediction from off-line handwriting analysisrdquoApplied Soft Computing vol 46 pp 980ndash990 2016

[5] P Drotar J Mekyska I Rektorova L Masarova Z SmekalandM Faundez-Zanuy ldquoEvaluation of handwriting kinematicsand pressure for differential diagnosis of Parkinsonrsquos diseaserdquoArtificial Intelligence in Medicine vol 67 pp 39ndash46 2016

[6] M S Baby A J Saji and C S Kumar ldquoParkinsons diseaseclassification using wavelet transform based feature extractionof gait datardquo in Proceedings of the 2017 IEEE InternationalConference on Circuit Power and Computing TechnologiesICCPCT 2017 India April 2017

[7] A Brahim L Khedher J M Gorriz et al ldquoA proposedcomputer-aided diagnosis system for Parkinsonrsquos disease classi-fication using 123I-FP-CIT imagingrdquo in Proceedings of the 3rdInternational Conference on Advanced Technologies for Signaland Image Processing ATSIP 2017 Morocco May 2017

[8] C R Pereira S A T Weber C Hook G H Rosa and J PPapa ldquoDeep learning-aided Parkinsonrsquos disease diagnosis fromhandwritten dynamicsrdquo in Proceedings of the 29th SIBGRAPIConference on Graphics Patterns and Images SIBGRAPI 2016pp 340ndash346 Brazil October 2016

[9] V V Shah S Goyal and H J Palanthandalam-Madapusi ldquoAperspective on the use of high-frequency stimulation in deepbrain stimulation for Parkinsonrsquos diseaserdquo in Proceedings of the2nd Indian Control Conference ICC 2016 pp 19ndash24 IndiaJanuary 2016

[10] N Zhi B K Jaeger A Gouldstone R Sipahi and S FrankldquoToward Monitoring Parkinsonrsquos Through Analysis of StaticHandwriting Samples A Quantitative Analytical Frameworkrdquo

8 Computational Intelligence and Neuroscience

IEEE Journal of Biomedical and Health Informatics vol 21 no2 pp 488ndash495 2017

[11] A-M Schiffer A J Nevado-Holgado A Johnen A RSchonberger G R Fink and R I Schubotz ldquoIntact actionsegmentation in Parkinsonrsquos disease Hypothesis testing usinga novel computational approachrdquoNeuropsychologia vol 78 pp29ndash40 2015

[12] R Guzman-Cabrera M Gomez-Sarabia M Torres-CisnerosM A Escobar-Acevedo and J R Guzman-Sepulveda ldquoParkin-sonrsquos disease Improved diagnosis using image processingrdquo inProceedings of the 2017 Photonics North (PN) pp 1-1 OttawaON Canada June 2017

[13] C R Pereira D R Pereira F A Silva et al ldquoA new computervision-based approach to aid the diagnosis of Parkinsonrsquosdiseaserdquo Computer Methods and Programs in Biomedicine vol136 pp 79ndash88 2016

[14] R Graca R S E Castro and J Cevada ldquoParkdetect Earlydiagnosing parkinsonrsquos diseaserdquo in Proceedings of the 9th IEEEInternational Symposium on Medical Measurements and Appli-cations IEEE MeMeA 2014 Portugal June 2014

[15] C R Pereira D R Pereira F A D Silva et al ldquoA steptowards the automated diagnosis of parkinsonrsquos disease Ana-lyzing handwritingmovementsrdquo in Proceedings of the 28th IEEEInternational Symposium on Computer-Based Medical SystemsCBMS 2015 pp 171ndash176 Brazil June 2015

[16] P Drotar J Mekyska I Rektorova L Masarova Z Smekal andM Faundez-Zanuy ldquoAnalysis of in-air movement in handwrit-ing A novel marker for Parkinsonrsquos diseaserdquo ComputerMethodsand Programs in Biomedicine vol 117 no 3 pp 405ndash411 2014

[17] P Drotar J Mekyska Z Smekal I Rektorova L MasarovaandM Faundez-Zanuy ldquoContribution of different handwritingmodalities to differential diagnosis of Parkinsonrsquos Diseaserdquoin Proceedings of the 2015 IEEE International Symposium onMedical Measurements and Applications MeMeA 2015 pp 344ndash348 Italy May 2015

[18] C Taleb M Khachab C Mokbel and L Likforman-SulemldquoFeature selection for an improved Parkinsonrsquos disease identi-fication based on handwritingrdquo in Proceedings of the 2017 1stInternational Workshop on Arabic Script Analysis and Recogni-tion (ASAR) pp 52ndash56 Nancy France April 2017

[19] Z Smekal J Mekyska I Rektorova and M Faundez-ZanuyldquoAnalysis of neurological disorders based on digital processingof speech and handwritten textrdquo in Proceedings of the 2013International Symposium on Signals Circuits and Systems ISSCS2013 Romania July 2013

[20] D Surangsrirat A Intarapanich C Thanawattano R Bhi-dayasiri S Petchrutchatachart and C Anan ldquoTremor assess-ment using spiral analysis in time-frequency domainrdquo inProceedings of the IEEE SoutheastCon 2013MovingAmerica intothe Future USA April 2013

[21] STheodoridis andK Koutroumbas Pattern Recognition fourthedition Edition Academic Press fourth edition Edition 2009

[22] J P Papa A X Falcao and C T N Suzuki ldquoSupervised patternclassification based on optimum-path forestrdquo InternationalJournal of Imaging Systems and Technology vol 19 no 2 pp120ndash131 2009

[23] J P Papa C T N Suzuki and A X Falcao A library forthe design of optimum-path forest classifiers 2014 httpwwwicunicampbrafalcaolibopfindexhtmlLibopf

[24] V N Vapnik Statistical Learning Theory John Wiley Sons Inc2009

[25] G L Bezerra Ramalho D S Ferreira P P Reboucas Filhoand F N S de Medeiros ldquoRotation-invariant feature extractionusing a structural co-occurrence matrixrdquoMeasurement vol 94pp 406ndash415 2016

[26] S L Gomes E D S Reboucas E C Neto et al ldquoEmbeddedreal-time speed limit sign recognition using image processingand machine learning techniquesrdquo Neural Computing andApplications vol 28 pp 573ndash584 2017

[27] P P R Filho A C D S Barros G L B Ramalho et alldquoAutomated recognition of lung diseases in CT images basedon the optimum-path forest classifierrdquo Neural Computing andApplications pp 1ndash14 2017

[28] P P Reboucas Filho E D S Reboucas L B Marinho R MSarmento J M R S Tavares and V H C de AlbuquerqueldquoAnalysis of human tissue densities A new approach to extractfeatures from medical imagesrdquo Pattern Recognition Letters vol94 pp 211ndash218 2017

[29] P P Reboucas Filho R M Sarmento G B Holanda and D deAlencar Lima ldquoNew approach to detect and classify stroke inskull CT images via analysis of brain tissue densitiesrdquoComputerMethods and Programs in Biomedicine vol 148 pp 27ndash43 2017

[30] E C Neto S L Gomes P P Reboucas Filho and V H CDe Albuquerque ldquoBrazilian vehicle identification using a newembedded plate recognition systemrdquoMeasurement vol 70 pp36ndash46 2015

[31] L B Marinho J S Almeida J W M Souza V H CAlbuquerque and P P Reboucas Filho ldquoA novel mobile robotlocalization approach based on topological maps using classi-fication with reject option in omnidirectional imagesrdquo ExpertSystems with Applications vol 72 pp 1ndash17 2017

[32] P P Reboucas Filho J C D Santos F N C Freitas et alldquoNew approach to evaluate a non-grain oriented electricalsteel electromagnetic performance using photomicrographicanalysis via digital image processingrdquo Journal of MaterialsResearch and Technology 2017

Computational Intelligence and Neuroscience 5

is applied again with the same structuring element to obtainthe real size of the ET as shown in Figure 4

In order to obtain the HT segmentation the image is alsosmoothed through the Median filter (5 times 5) to eliminatethe noise picked up during the examsThen the handwritingexam is converted to the grayscale after which the Otsuthreshold is applied Finally we apply a difference operationbetween the grayscale image and the ET These steps areshown in Figure 4

The second step in our proposal converts the segmentedexams to the grayscale for the next step The third stepis feature extraction from the images obtained after thesegmentation and conversion to the grayscale These imagesare used as the input to SCM as showed in Figure 2 Thefeature extraction through the SCM is a method to analyzethe relationship between signals in this case in a two-dimensional space

A slight modification was made to the original SCMmethod The SCM has two input parameters an image anda filter We replaced the filter with another image The newconfiguration of the input parameters is presented as anexample in Figure 3This proposal is intended to enhance thedifferences between the two input images by computing thesimilarity approximation between the patientrsquos trace and theexam templateThere is no need to configure a filter as in thisSCMmethod the filter was replaced for another image

Three combinations of three images were proposed as theSCM inputs which were then used for a complete analysis(i) handwriting exam and handwritten trace (ii) handwritingexam and exam template and (iii) handwritten trace andexam template

The scalar attributes obtained through the SCMmethodare computed by the SCM generated between the inputimages These attributes are divided into three groups sta-tistical group information group and divergent group [25]All these attributes are computed based on the SCM matrixrepresented by M = m119894119895 and some are related to themarginal distribution P of the SCM A brief description ofeach attribute is given below

(i) Correlation (COR) measures how the information iscorrelated The COR is given by

COR = 119873minus1sum119894119895=0

m119894119895(119894 minus 120583119894) (119895 minus 120583119895)radic12059011989421205901198952 isin [minus1 1] (5)

where 120583119894 and 120583119895 are the average value of rows andcolumns of M 120590119894 and 120590119895 are the standard deviationof rows and columns of M

(ii) Inverse difference moment (IDM) measures thehomogeneity and it is given by

IDM = 119873minus1sum119894119895=0

m1198941198951 + (1003816100381610038161003816119894 minus 1198951003816100381610038161003816) isin [0 1] (6)

(iii) Entropy (ENT) the randomness of the information ismeasured and it is given by

ENT = minus119873minus1sum119894119895=0

m119894119895 log (m119894119895) (7)

(iv) Chi-square distance (CSD) since the P0119894 = m119894119895 = 119895and P119890119894 = sum119873minus1119895=0 m119894119895 gt= 119894

CSD = 119873minus1sum119894=0

(P0119894 minus P119890119894 )2P119890119894 (8)

(v) Chi-square distance ratio (CSR) with the distribu-tions of the quadrants I PI

119894 = sum1198732minus1119894=0 sum1198732minus1119895=0 m119894119895 andIII PIII119894 = sum119873minus1119894=1198732sum119873minus1119895=1198732m119894119895 the CSR is given by

CSR = 119873minus1sum119894=0

(PI119894 minus P119898119894 )2P119898119894 (9)

where P119898119894 = (PI119894 + PIII119894 )2

(vi) Mean absolute difference ratio (MDR) calculatesthe statistical dispersion where MDmin =min(MD119901119888 MD119901119903) MDmax = max(MD119901119888 MD119901119903)and MD119901 = sum119873minus1119894=0 sum119873minus1119895=0 P(119878119894)P(119878119895)|119878119894 minus 119878119895| TheMDRis given by

MDR = MDminMDmax (10)

where 119878119894 119878119895 119894 = 0 119873 minus 1 are the indexes of thenonzero values of the marginal distribution P

(vii) Divergence of Kullback Leibler (119863KL) measures theinformation gain between bordering distributionsand it is given by

119863KL = 119873minus1sum119894=0

log(P119888119894P119903119894)P119888119894 (11)

where P119888119894 P119903119894 = 0 where P119903119894 = 0(viii) Complementary absolute difference (CAD) compares

the two probability distributions P119888119894 and P119903119894 The CAD

is given by

CAD = 1 minus 119873minus1sum119894=0

1003816100381610038161003816P119888119894 minus P119903119894 1003816100381610038161003816 (12)

At the end of the SCMprocess we have the attributes to beapplied as pattern recognition inputs to anymachine learningmethod

6 Computational Intelligence and Neuroscience

Table 1 Results from the best classifiers and combinations

Meander Spiral MSAcc () Acc () Acc ()

BayesahArr c 7277 plusmn 991 6957 plusmn 1107 6595 plusmn 205ahArr b 7511 plusmn 399 7636 plusmn 426 6838 plusmn 218chArr b 7772 plusmn 427 8201 plusmn 553 7139 plusmn 239

OPFahArr c 7554 plusmn 376 6973 plusmn 363 6428 plusmn 205ahArr b 6859 plusmn 376 7049 plusmn 363 6113 plusmn 257chArr b 7750 plusmn 275 7571 plusmn 439 6721 plusmn 198

SVMahArr c 8005 plusmn 180 7804 plusmn 274 6763 plusmn 273ahArr b 7500 plusmn 290 7804 plusmn 285 6530 plusmn 292chArr b 8223 plusmn 302 8554 plusmn 362 7413 plusmn 227

6 Experimental Setup

We conducted some experiments with the handwritingexams used by Pereira et al [13] These exams concern thepatientrsquos ability to underline the two exam templates spiraland meander First the spiral and meander exam attributeswere extracted by the proposed approach presented in Sec-tion 5 Then we applied three machine learning methodsNaıve Bayes OPF and SVM in 3 different experiments (i)considering only the spiral exam attributes (ii) consideringonly themeander exam attributes and (iii) considering spiraland meander exam attributes together That is we evaluatethe proposed approach using the two handwriting formatsseparately and also together After that we compared theresults with those of Pereira et al [13]

The number of HandPD dataset samples used in all ofthe experiments are divided into 368 spiral exam samplesand 368meander exam samples resulting in 736 samplesWeemphasize that each subset is divided into 296 samples fromthe CG and 72 samples from the PG

All the three experiments were conducted with 75 forthe training set 25 for testing set and the remaining 25 forthe validation set We applied a cross-validation with 20 runsfor the reliability of the resultsThe setup of the OPF classifierwas configuredwith the Euclidean distance and the SVMwiththe radial basis function The SVM and OPF classifiers wereautomatically optimised by choosing the optimal parametersfrom each method Parameters are considered optimal whenthe cross-validation estimates the minimal error [26ndash32]

Accuracy was used to classify the machine learningmethods This metric is defined using the terms obtained inthe confusion matrix generated after applying the machinelearning methods and refers to the closeness of a measuredvalue to a standard or known value

7 Results and Discussion

In this section we provide an analysis of the results usingthe handwriting exams from Pereira et al [13] These exams

present the patientrsquos ability to underline the exam templateThe results were obtained by applying machine learningmethods to the features extracted from cooccurrence matrixobtained over the SCM extractor used in this work

Table 1 presents the classification results of the combi-nations between the images obtained in the segmentation(ET and HT) and the handwritten exam using SCM wherethe meander and spiral formats were evaluated separatelyand together The handwritten exam exam template andhandwritten trace are represented in Table 1 as a b and crespectively

Table 1 also shows that the combination of the meanderand spiral exam obtained the lowest accuracy rates The verysignificant difference between the spiral andmeander formatsjustifies these low rates

Another important factor observed in the best resultsobtained with the classifiers in Table 1 was the predominanceof the best results using the combination of the handwrittentrace and the exam template which proves that these two arethe most similar

The results of this work were compared with the resultsobtained byPereira et al approach [13] inTable 2which showseach classifier with subdivisions for the experiments usinghandwriting exams in the formats of meanders spirals andthe combination of the meanders and spirals Immediatelyafter that there is a new subdivision between the resultsobtained with our proposal and those of Pereira et al [13]In Table 2 the highest accuracy results are in underline fontand the lowest ones in italic font for each of the classifiersAlso highlight in bold are the highest and the lowest accuracyresults of both approaches and experiments

The results obtained in this paper proved to be superiorin all of the experiments in comparison to those of Pereiraproposal [13] The highest accuracy was 8554 using theapproach proposed This highest result was provided withthe SVM classifier using the handwriting in a spiral formatwith the combination of the handwritten trace and theexam template The lowest accuracy among the best resultspresented in Table 2 was 4579

Computational Intelligence and Neuroscience 7

Table 2 Comparison between the best results of this paper and the best results of the Pereira et al approach [13]

Classif Database Feature extractor Accuracy ()

Bayes

Meander Pereira et al approach [13] 5920 plusmn 478Proposed approach 7772 plusmn 427

Spiral Pereira et al approach [13] 6423 plusmn 711Proposed approach 8201 plusmn 553

MS Pereira et al approach [13] 4579 plusmn 415Proposed approach 7139 plusmn 239

OPF

Meander Pereira et al approach [13] 5754 plusmn 635Proposed approach 7750 plusmn 275

Spiral Pereira et al approach [13] 5248 plusmn 532Proposed approach 7571 plusmn 439

MS Pereira et al approach [13] 5586 plusmn 363Proposed approach 6721 plusmn 198

SVM

Meander Pereira et al approach [13] 6672 plusmn 533Proposed approach 8223 plusmn 302

Spiral Pereira et al approach [13] 5016 plusmn 171Proposed approach 8554 plusmn 362

MS Pereira et al approach [13] 5861 plusmn 284Proposed approach 7413 plusmn 227

8 Conclusion

The proposal of this paper is based on the patientrsquos trace andthe exam template using the SCM method for a similarityapproximation The SVM classifier with the RBF kernel andhandwriting in the spiral format proved being the mostpromising for this application This configuration had anaccuracy of 8554 for the combination of the handwrittentrace and the exam template in the feature extraction Theresults obtained in this paperwere 2131superior to the bestresult achieved by Pereira et al approach [13]

We conclude that this is promising approach to help in thediagnosis of PD Another advantage of this proposal is thatthere is no need to configure a filter to obtain the structuredcooccurrence matrix facilitating its application

These results encourage us to propose future works forhandwriting feature extraction

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

Pedro P Reboucas Filho acknowledges the sponsorship fromthe IFCE via Grant PROINFRA PPG2017 Joao W Mde Souza acknowledges the CNPq for financial supportJefferson S Almeida acknowledges the FUNCAP for financialsupport

References

[1] WHO Neurological Disorders Public Health Challenges WorldHealth Organization 2006

[2] J Parkinson ldquoAn essay on the shaking palsy 1817rdquo The Journalof Neuropsychiatry and Clinical Neurosciences vol 14 no 2 pp223ndash236 2002

[3] J Chen Z Lei L Liu G Zhao and M Pietikainen ldquoRoLoDRobust local descriptors for computer visionrdquoNeurocomputingvol 184 pp 1-2 2016

[4] N Bouadjenek H Nemmour and Y Chibani ldquoRobust soft-biometrics prediction from off-line handwriting analysisrdquoApplied Soft Computing vol 46 pp 980ndash990 2016

[5] P Drotar J Mekyska I Rektorova L Masarova Z SmekalandM Faundez-Zanuy ldquoEvaluation of handwriting kinematicsand pressure for differential diagnosis of Parkinsonrsquos diseaserdquoArtificial Intelligence in Medicine vol 67 pp 39ndash46 2016

[6] M S Baby A J Saji and C S Kumar ldquoParkinsons diseaseclassification using wavelet transform based feature extractionof gait datardquo in Proceedings of the 2017 IEEE InternationalConference on Circuit Power and Computing TechnologiesICCPCT 2017 India April 2017

[7] A Brahim L Khedher J M Gorriz et al ldquoA proposedcomputer-aided diagnosis system for Parkinsonrsquos disease classi-fication using 123I-FP-CIT imagingrdquo in Proceedings of the 3rdInternational Conference on Advanced Technologies for Signaland Image Processing ATSIP 2017 Morocco May 2017

[8] C R Pereira S A T Weber C Hook G H Rosa and J PPapa ldquoDeep learning-aided Parkinsonrsquos disease diagnosis fromhandwritten dynamicsrdquo in Proceedings of the 29th SIBGRAPIConference on Graphics Patterns and Images SIBGRAPI 2016pp 340ndash346 Brazil October 2016

[9] V V Shah S Goyal and H J Palanthandalam-Madapusi ldquoAperspective on the use of high-frequency stimulation in deepbrain stimulation for Parkinsonrsquos diseaserdquo in Proceedings of the2nd Indian Control Conference ICC 2016 pp 19ndash24 IndiaJanuary 2016

[10] N Zhi B K Jaeger A Gouldstone R Sipahi and S FrankldquoToward Monitoring Parkinsonrsquos Through Analysis of StaticHandwriting Samples A Quantitative Analytical Frameworkrdquo

8 Computational Intelligence and Neuroscience

IEEE Journal of Biomedical and Health Informatics vol 21 no2 pp 488ndash495 2017

[11] A-M Schiffer A J Nevado-Holgado A Johnen A RSchonberger G R Fink and R I Schubotz ldquoIntact actionsegmentation in Parkinsonrsquos disease Hypothesis testing usinga novel computational approachrdquoNeuropsychologia vol 78 pp29ndash40 2015

[12] R Guzman-Cabrera M Gomez-Sarabia M Torres-CisnerosM A Escobar-Acevedo and J R Guzman-Sepulveda ldquoParkin-sonrsquos disease Improved diagnosis using image processingrdquo inProceedings of the 2017 Photonics North (PN) pp 1-1 OttawaON Canada June 2017

[13] C R Pereira D R Pereira F A Silva et al ldquoA new computervision-based approach to aid the diagnosis of Parkinsonrsquosdiseaserdquo Computer Methods and Programs in Biomedicine vol136 pp 79ndash88 2016

[14] R Graca R S E Castro and J Cevada ldquoParkdetect Earlydiagnosing parkinsonrsquos diseaserdquo in Proceedings of the 9th IEEEInternational Symposium on Medical Measurements and Appli-cations IEEE MeMeA 2014 Portugal June 2014

[15] C R Pereira D R Pereira F A D Silva et al ldquoA steptowards the automated diagnosis of parkinsonrsquos disease Ana-lyzing handwritingmovementsrdquo in Proceedings of the 28th IEEEInternational Symposium on Computer-Based Medical SystemsCBMS 2015 pp 171ndash176 Brazil June 2015

[16] P Drotar J Mekyska I Rektorova L Masarova Z Smekal andM Faundez-Zanuy ldquoAnalysis of in-air movement in handwrit-ing A novel marker for Parkinsonrsquos diseaserdquo ComputerMethodsand Programs in Biomedicine vol 117 no 3 pp 405ndash411 2014

[17] P Drotar J Mekyska Z Smekal I Rektorova L MasarovaandM Faundez-Zanuy ldquoContribution of different handwritingmodalities to differential diagnosis of Parkinsonrsquos Diseaserdquoin Proceedings of the 2015 IEEE International Symposium onMedical Measurements and Applications MeMeA 2015 pp 344ndash348 Italy May 2015

[18] C Taleb M Khachab C Mokbel and L Likforman-SulemldquoFeature selection for an improved Parkinsonrsquos disease identi-fication based on handwritingrdquo in Proceedings of the 2017 1stInternational Workshop on Arabic Script Analysis and Recogni-tion (ASAR) pp 52ndash56 Nancy France April 2017

[19] Z Smekal J Mekyska I Rektorova and M Faundez-ZanuyldquoAnalysis of neurological disorders based on digital processingof speech and handwritten textrdquo in Proceedings of the 2013International Symposium on Signals Circuits and Systems ISSCS2013 Romania July 2013

[20] D Surangsrirat A Intarapanich C Thanawattano R Bhi-dayasiri S Petchrutchatachart and C Anan ldquoTremor assess-ment using spiral analysis in time-frequency domainrdquo inProceedings of the IEEE SoutheastCon 2013MovingAmerica intothe Future USA April 2013

[21] STheodoridis andK Koutroumbas Pattern Recognition fourthedition Edition Academic Press fourth edition Edition 2009

[22] J P Papa A X Falcao and C T N Suzuki ldquoSupervised patternclassification based on optimum-path forestrdquo InternationalJournal of Imaging Systems and Technology vol 19 no 2 pp120ndash131 2009

[23] J P Papa C T N Suzuki and A X Falcao A library forthe design of optimum-path forest classifiers 2014 httpwwwicunicampbrafalcaolibopfindexhtmlLibopf

[24] V N Vapnik Statistical Learning Theory John Wiley Sons Inc2009

[25] G L Bezerra Ramalho D S Ferreira P P Reboucas Filhoand F N S de Medeiros ldquoRotation-invariant feature extractionusing a structural co-occurrence matrixrdquoMeasurement vol 94pp 406ndash415 2016

[26] S L Gomes E D S Reboucas E C Neto et al ldquoEmbeddedreal-time speed limit sign recognition using image processingand machine learning techniquesrdquo Neural Computing andApplications vol 28 pp 573ndash584 2017

[27] P P R Filho A C D S Barros G L B Ramalho et alldquoAutomated recognition of lung diseases in CT images basedon the optimum-path forest classifierrdquo Neural Computing andApplications pp 1ndash14 2017

[28] P P Reboucas Filho E D S Reboucas L B Marinho R MSarmento J M R S Tavares and V H C de AlbuquerqueldquoAnalysis of human tissue densities A new approach to extractfeatures from medical imagesrdquo Pattern Recognition Letters vol94 pp 211ndash218 2017

[29] P P Reboucas Filho R M Sarmento G B Holanda and D deAlencar Lima ldquoNew approach to detect and classify stroke inskull CT images via analysis of brain tissue densitiesrdquoComputerMethods and Programs in Biomedicine vol 148 pp 27ndash43 2017

[30] E C Neto S L Gomes P P Reboucas Filho and V H CDe Albuquerque ldquoBrazilian vehicle identification using a newembedded plate recognition systemrdquoMeasurement vol 70 pp36ndash46 2015

[31] L B Marinho J S Almeida J W M Souza V H CAlbuquerque and P P Reboucas Filho ldquoA novel mobile robotlocalization approach based on topological maps using classi-fication with reject option in omnidirectional imagesrdquo ExpertSystems with Applications vol 72 pp 1ndash17 2017

[32] P P Reboucas Filho J C D Santos F N C Freitas et alldquoNew approach to evaluate a non-grain oriented electricalsteel electromagnetic performance using photomicrographicanalysis via digital image processingrdquo Journal of MaterialsResearch and Technology 2017

6 Computational Intelligence and Neuroscience

Table 1 Results from the best classifiers and combinations

Meander Spiral MSAcc () Acc () Acc ()

BayesahArr c 7277 plusmn 991 6957 plusmn 1107 6595 plusmn 205ahArr b 7511 plusmn 399 7636 plusmn 426 6838 plusmn 218chArr b 7772 plusmn 427 8201 plusmn 553 7139 plusmn 239

OPFahArr c 7554 plusmn 376 6973 plusmn 363 6428 plusmn 205ahArr b 6859 plusmn 376 7049 plusmn 363 6113 plusmn 257chArr b 7750 plusmn 275 7571 plusmn 439 6721 plusmn 198

SVMahArr c 8005 plusmn 180 7804 plusmn 274 6763 plusmn 273ahArr b 7500 plusmn 290 7804 plusmn 285 6530 plusmn 292chArr b 8223 plusmn 302 8554 plusmn 362 7413 plusmn 227

6 Experimental Setup

We conducted some experiments with the handwritingexams used by Pereira et al [13] These exams concern thepatientrsquos ability to underline the two exam templates spiraland meander First the spiral and meander exam attributeswere extracted by the proposed approach presented in Sec-tion 5 Then we applied three machine learning methodsNaıve Bayes OPF and SVM in 3 different experiments (i)considering only the spiral exam attributes (ii) consideringonly themeander exam attributes and (iii) considering spiraland meander exam attributes together That is we evaluatethe proposed approach using the two handwriting formatsseparately and also together After that we compared theresults with those of Pereira et al [13]

The number of HandPD dataset samples used in all ofthe experiments are divided into 368 spiral exam samplesand 368meander exam samples resulting in 736 samplesWeemphasize that each subset is divided into 296 samples fromthe CG and 72 samples from the PG

All the three experiments were conducted with 75 forthe training set 25 for testing set and the remaining 25 forthe validation set We applied a cross-validation with 20 runsfor the reliability of the resultsThe setup of the OPF classifierwas configuredwith the Euclidean distance and the SVMwiththe radial basis function The SVM and OPF classifiers wereautomatically optimised by choosing the optimal parametersfrom each method Parameters are considered optimal whenthe cross-validation estimates the minimal error [26ndash32]

Accuracy was used to classify the machine learningmethods This metric is defined using the terms obtained inthe confusion matrix generated after applying the machinelearning methods and refers to the closeness of a measuredvalue to a standard or known value

7 Results and Discussion

In this section we provide an analysis of the results usingthe handwriting exams from Pereira et al [13] These exams

present the patientrsquos ability to underline the exam templateThe results were obtained by applying machine learningmethods to the features extracted from cooccurrence matrixobtained over the SCM extractor used in this work

Table 1 presents the classification results of the combi-nations between the images obtained in the segmentation(ET and HT) and the handwritten exam using SCM wherethe meander and spiral formats were evaluated separatelyand together The handwritten exam exam template andhandwritten trace are represented in Table 1 as a b and crespectively

Table 1 also shows that the combination of the meanderand spiral exam obtained the lowest accuracy rates The verysignificant difference between the spiral andmeander formatsjustifies these low rates

Another important factor observed in the best resultsobtained with the classifiers in Table 1 was the predominanceof the best results using the combination of the handwrittentrace and the exam template which proves that these two arethe most similar

The results of this work were compared with the resultsobtained byPereira et al approach [13] inTable 2which showseach classifier with subdivisions for the experiments usinghandwriting exams in the formats of meanders spirals andthe combination of the meanders and spirals Immediatelyafter that there is a new subdivision between the resultsobtained with our proposal and those of Pereira et al [13]In Table 2 the highest accuracy results are in underline fontand the lowest ones in italic font for each of the classifiersAlso highlight in bold are the highest and the lowest accuracyresults of both approaches and experiments

The results obtained in this paper proved to be superiorin all of the experiments in comparison to those of Pereiraproposal [13] The highest accuracy was 8554 using theapproach proposed This highest result was provided withthe SVM classifier using the handwriting in a spiral formatwith the combination of the handwritten trace and theexam template The lowest accuracy among the best resultspresented in Table 2 was 4579

Computational Intelligence and Neuroscience 7

Table 2 Comparison between the best results of this paper and the best results of the Pereira et al approach [13]

Classif Database Feature extractor Accuracy ()

Bayes

Meander Pereira et al approach [13] 5920 plusmn 478Proposed approach 7772 plusmn 427

Spiral Pereira et al approach [13] 6423 plusmn 711Proposed approach 8201 plusmn 553

MS Pereira et al approach [13] 4579 plusmn 415Proposed approach 7139 plusmn 239

OPF

Meander Pereira et al approach [13] 5754 plusmn 635Proposed approach 7750 plusmn 275

Spiral Pereira et al approach [13] 5248 plusmn 532Proposed approach 7571 plusmn 439

MS Pereira et al approach [13] 5586 plusmn 363Proposed approach 6721 plusmn 198

SVM

Meander Pereira et al approach [13] 6672 plusmn 533Proposed approach 8223 plusmn 302

Spiral Pereira et al approach [13] 5016 plusmn 171Proposed approach 8554 plusmn 362

MS Pereira et al approach [13] 5861 plusmn 284Proposed approach 7413 plusmn 227

8 Conclusion

The proposal of this paper is based on the patientrsquos trace andthe exam template using the SCM method for a similarityapproximation The SVM classifier with the RBF kernel andhandwriting in the spiral format proved being the mostpromising for this application This configuration had anaccuracy of 8554 for the combination of the handwrittentrace and the exam template in the feature extraction Theresults obtained in this paperwere 2131superior to the bestresult achieved by Pereira et al approach [13]

We conclude that this is promising approach to help in thediagnosis of PD Another advantage of this proposal is thatthere is no need to configure a filter to obtain the structuredcooccurrence matrix facilitating its application

These results encourage us to propose future works forhandwriting feature extraction

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

Pedro P Reboucas Filho acknowledges the sponsorship fromthe IFCE via Grant PROINFRA PPG2017 Joao W Mde Souza acknowledges the CNPq for financial supportJefferson S Almeida acknowledges the FUNCAP for financialsupport

References

[1] WHO Neurological Disorders Public Health Challenges WorldHealth Organization 2006

[2] J Parkinson ldquoAn essay on the shaking palsy 1817rdquo The Journalof Neuropsychiatry and Clinical Neurosciences vol 14 no 2 pp223ndash236 2002

[3] J Chen Z Lei L Liu G Zhao and M Pietikainen ldquoRoLoDRobust local descriptors for computer visionrdquoNeurocomputingvol 184 pp 1-2 2016

[4] N Bouadjenek H Nemmour and Y Chibani ldquoRobust soft-biometrics prediction from off-line handwriting analysisrdquoApplied Soft Computing vol 46 pp 980ndash990 2016

[5] P Drotar J Mekyska I Rektorova L Masarova Z SmekalandM Faundez-Zanuy ldquoEvaluation of handwriting kinematicsand pressure for differential diagnosis of Parkinsonrsquos diseaserdquoArtificial Intelligence in Medicine vol 67 pp 39ndash46 2016

[6] M S Baby A J Saji and C S Kumar ldquoParkinsons diseaseclassification using wavelet transform based feature extractionof gait datardquo in Proceedings of the 2017 IEEE InternationalConference on Circuit Power and Computing TechnologiesICCPCT 2017 India April 2017

[7] A Brahim L Khedher J M Gorriz et al ldquoA proposedcomputer-aided diagnosis system for Parkinsonrsquos disease classi-fication using 123I-FP-CIT imagingrdquo in Proceedings of the 3rdInternational Conference on Advanced Technologies for Signaland Image Processing ATSIP 2017 Morocco May 2017

[8] C R Pereira S A T Weber C Hook G H Rosa and J PPapa ldquoDeep learning-aided Parkinsonrsquos disease diagnosis fromhandwritten dynamicsrdquo in Proceedings of the 29th SIBGRAPIConference on Graphics Patterns and Images SIBGRAPI 2016pp 340ndash346 Brazil October 2016

[9] V V Shah S Goyal and H J Palanthandalam-Madapusi ldquoAperspective on the use of high-frequency stimulation in deepbrain stimulation for Parkinsonrsquos diseaserdquo in Proceedings of the2nd Indian Control Conference ICC 2016 pp 19ndash24 IndiaJanuary 2016

[10] N Zhi B K Jaeger A Gouldstone R Sipahi and S FrankldquoToward Monitoring Parkinsonrsquos Through Analysis of StaticHandwriting Samples A Quantitative Analytical Frameworkrdquo

8 Computational Intelligence and Neuroscience

IEEE Journal of Biomedical and Health Informatics vol 21 no2 pp 488ndash495 2017

[11] A-M Schiffer A J Nevado-Holgado A Johnen A RSchonberger G R Fink and R I Schubotz ldquoIntact actionsegmentation in Parkinsonrsquos disease Hypothesis testing usinga novel computational approachrdquoNeuropsychologia vol 78 pp29ndash40 2015

[12] R Guzman-Cabrera M Gomez-Sarabia M Torres-CisnerosM A Escobar-Acevedo and J R Guzman-Sepulveda ldquoParkin-sonrsquos disease Improved diagnosis using image processingrdquo inProceedings of the 2017 Photonics North (PN) pp 1-1 OttawaON Canada June 2017

[13] C R Pereira D R Pereira F A Silva et al ldquoA new computervision-based approach to aid the diagnosis of Parkinsonrsquosdiseaserdquo Computer Methods and Programs in Biomedicine vol136 pp 79ndash88 2016

[14] R Graca R S E Castro and J Cevada ldquoParkdetect Earlydiagnosing parkinsonrsquos diseaserdquo in Proceedings of the 9th IEEEInternational Symposium on Medical Measurements and Appli-cations IEEE MeMeA 2014 Portugal June 2014

[15] C R Pereira D R Pereira F A D Silva et al ldquoA steptowards the automated diagnosis of parkinsonrsquos disease Ana-lyzing handwritingmovementsrdquo in Proceedings of the 28th IEEEInternational Symposium on Computer-Based Medical SystemsCBMS 2015 pp 171ndash176 Brazil June 2015

[16] P Drotar J Mekyska I Rektorova L Masarova Z Smekal andM Faundez-Zanuy ldquoAnalysis of in-air movement in handwrit-ing A novel marker for Parkinsonrsquos diseaserdquo ComputerMethodsand Programs in Biomedicine vol 117 no 3 pp 405ndash411 2014

[17] P Drotar J Mekyska Z Smekal I Rektorova L MasarovaandM Faundez-Zanuy ldquoContribution of different handwritingmodalities to differential diagnosis of Parkinsonrsquos Diseaserdquoin Proceedings of the 2015 IEEE International Symposium onMedical Measurements and Applications MeMeA 2015 pp 344ndash348 Italy May 2015

[18] C Taleb M Khachab C Mokbel and L Likforman-SulemldquoFeature selection for an improved Parkinsonrsquos disease identi-fication based on handwritingrdquo in Proceedings of the 2017 1stInternational Workshop on Arabic Script Analysis and Recogni-tion (ASAR) pp 52ndash56 Nancy France April 2017

[19] Z Smekal J Mekyska I Rektorova and M Faundez-ZanuyldquoAnalysis of neurological disorders based on digital processingof speech and handwritten textrdquo in Proceedings of the 2013International Symposium on Signals Circuits and Systems ISSCS2013 Romania July 2013

[20] D Surangsrirat A Intarapanich C Thanawattano R Bhi-dayasiri S Petchrutchatachart and C Anan ldquoTremor assess-ment using spiral analysis in time-frequency domainrdquo inProceedings of the IEEE SoutheastCon 2013MovingAmerica intothe Future USA April 2013

[21] STheodoridis andK Koutroumbas Pattern Recognition fourthedition Edition Academic Press fourth edition Edition 2009

[22] J P Papa A X Falcao and C T N Suzuki ldquoSupervised patternclassification based on optimum-path forestrdquo InternationalJournal of Imaging Systems and Technology vol 19 no 2 pp120ndash131 2009

[23] J P Papa C T N Suzuki and A X Falcao A library forthe design of optimum-path forest classifiers 2014 httpwwwicunicampbrafalcaolibopfindexhtmlLibopf

[24] V N Vapnik Statistical Learning Theory John Wiley Sons Inc2009

[25] G L Bezerra Ramalho D S Ferreira P P Reboucas Filhoand F N S de Medeiros ldquoRotation-invariant feature extractionusing a structural co-occurrence matrixrdquoMeasurement vol 94pp 406ndash415 2016

[26] S L Gomes E D S Reboucas E C Neto et al ldquoEmbeddedreal-time speed limit sign recognition using image processingand machine learning techniquesrdquo Neural Computing andApplications vol 28 pp 573ndash584 2017

[27] P P R Filho A C D S Barros G L B Ramalho et alldquoAutomated recognition of lung diseases in CT images basedon the optimum-path forest classifierrdquo Neural Computing andApplications pp 1ndash14 2017

[28] P P Reboucas Filho E D S Reboucas L B Marinho R MSarmento J M R S Tavares and V H C de AlbuquerqueldquoAnalysis of human tissue densities A new approach to extractfeatures from medical imagesrdquo Pattern Recognition Letters vol94 pp 211ndash218 2017

[29] P P Reboucas Filho R M Sarmento G B Holanda and D deAlencar Lima ldquoNew approach to detect and classify stroke inskull CT images via analysis of brain tissue densitiesrdquoComputerMethods and Programs in Biomedicine vol 148 pp 27ndash43 2017

[30] E C Neto S L Gomes P P Reboucas Filho and V H CDe Albuquerque ldquoBrazilian vehicle identification using a newembedded plate recognition systemrdquoMeasurement vol 70 pp36ndash46 2015

[31] L B Marinho J S Almeida J W M Souza V H CAlbuquerque and P P Reboucas Filho ldquoA novel mobile robotlocalization approach based on topological maps using classi-fication with reject option in omnidirectional imagesrdquo ExpertSystems with Applications vol 72 pp 1ndash17 2017

[32] P P Reboucas Filho J C D Santos F N C Freitas et alldquoNew approach to evaluate a non-grain oriented electricalsteel electromagnetic performance using photomicrographicanalysis via digital image processingrdquo Journal of MaterialsResearch and Technology 2017

Computational Intelligence and Neuroscience 7

Table 2 Comparison between the best results of this paper and the best results of the Pereira et al approach [13]

Classif Database Feature extractor Accuracy ()

Bayes

Meander Pereira et al approach [13] 5920 plusmn 478Proposed approach 7772 plusmn 427

Spiral Pereira et al approach [13] 6423 plusmn 711Proposed approach 8201 plusmn 553

MS Pereira et al approach [13] 4579 plusmn 415Proposed approach 7139 plusmn 239

OPF

Meander Pereira et al approach [13] 5754 plusmn 635Proposed approach 7750 plusmn 275

Spiral Pereira et al approach [13] 5248 plusmn 532Proposed approach 7571 plusmn 439

MS Pereira et al approach [13] 5586 plusmn 363Proposed approach 6721 plusmn 198

SVM

Meander Pereira et al approach [13] 6672 plusmn 533Proposed approach 8223 plusmn 302

Spiral Pereira et al approach [13] 5016 plusmn 171Proposed approach 8554 plusmn 362

MS Pereira et al approach [13] 5861 plusmn 284Proposed approach 7413 plusmn 227

8 Conclusion

The proposal of this paper is based on the patientrsquos trace andthe exam template using the SCM method for a similarityapproximation The SVM classifier with the RBF kernel andhandwriting in the spiral format proved being the mostpromising for this application This configuration had anaccuracy of 8554 for the combination of the handwrittentrace and the exam template in the feature extraction Theresults obtained in this paperwere 2131superior to the bestresult achieved by Pereira et al approach [13]

We conclude that this is promising approach to help in thediagnosis of PD Another advantage of this proposal is thatthere is no need to configure a filter to obtain the structuredcooccurrence matrix facilitating its application

These results encourage us to propose future works forhandwriting feature extraction

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

Pedro P Reboucas Filho acknowledges the sponsorship fromthe IFCE via Grant PROINFRA PPG2017 Joao W Mde Souza acknowledges the CNPq for financial supportJefferson S Almeida acknowledges the FUNCAP for financialsupport

References

[1] WHO Neurological Disorders Public Health Challenges WorldHealth Organization 2006

[2] J Parkinson ldquoAn essay on the shaking palsy 1817rdquo The Journalof Neuropsychiatry and Clinical Neurosciences vol 14 no 2 pp223ndash236 2002

[3] J Chen Z Lei L Liu G Zhao and M Pietikainen ldquoRoLoDRobust local descriptors for computer visionrdquoNeurocomputingvol 184 pp 1-2 2016

[4] N Bouadjenek H Nemmour and Y Chibani ldquoRobust soft-biometrics prediction from off-line handwriting analysisrdquoApplied Soft Computing vol 46 pp 980ndash990 2016

[5] P Drotar J Mekyska I Rektorova L Masarova Z SmekalandM Faundez-Zanuy ldquoEvaluation of handwriting kinematicsand pressure for differential diagnosis of Parkinsonrsquos diseaserdquoArtificial Intelligence in Medicine vol 67 pp 39ndash46 2016

[6] M S Baby A J Saji and C S Kumar ldquoParkinsons diseaseclassification using wavelet transform based feature extractionof gait datardquo in Proceedings of the 2017 IEEE InternationalConference on Circuit Power and Computing TechnologiesICCPCT 2017 India April 2017

[7] A Brahim L Khedher J M Gorriz et al ldquoA proposedcomputer-aided diagnosis system for Parkinsonrsquos disease classi-fication using 123I-FP-CIT imagingrdquo in Proceedings of the 3rdInternational Conference on Advanced Technologies for Signaland Image Processing ATSIP 2017 Morocco May 2017

[8] C R Pereira S A T Weber C Hook G H Rosa and J PPapa ldquoDeep learning-aided Parkinsonrsquos disease diagnosis fromhandwritten dynamicsrdquo in Proceedings of the 29th SIBGRAPIConference on Graphics Patterns and Images SIBGRAPI 2016pp 340ndash346 Brazil October 2016

[9] V V Shah S Goyal and H J Palanthandalam-Madapusi ldquoAperspective on the use of high-frequency stimulation in deepbrain stimulation for Parkinsonrsquos diseaserdquo in Proceedings of the2nd Indian Control Conference ICC 2016 pp 19ndash24 IndiaJanuary 2016

[10] N Zhi B K Jaeger A Gouldstone R Sipahi and S FrankldquoToward Monitoring Parkinsonrsquos Through Analysis of StaticHandwriting Samples A Quantitative Analytical Frameworkrdquo

8 Computational Intelligence and Neuroscience

IEEE Journal of Biomedical and Health Informatics vol 21 no2 pp 488ndash495 2017

[11] A-M Schiffer A J Nevado-Holgado A Johnen A RSchonberger G R Fink and R I Schubotz ldquoIntact actionsegmentation in Parkinsonrsquos disease Hypothesis testing usinga novel computational approachrdquoNeuropsychologia vol 78 pp29ndash40 2015

[12] R Guzman-Cabrera M Gomez-Sarabia M Torres-CisnerosM A Escobar-Acevedo and J R Guzman-Sepulveda ldquoParkin-sonrsquos disease Improved diagnosis using image processingrdquo inProceedings of the 2017 Photonics North (PN) pp 1-1 OttawaON Canada June 2017

[13] C R Pereira D R Pereira F A Silva et al ldquoA new computervision-based approach to aid the diagnosis of Parkinsonrsquosdiseaserdquo Computer Methods and Programs in Biomedicine vol136 pp 79ndash88 2016

[14] R Graca R S E Castro and J Cevada ldquoParkdetect Earlydiagnosing parkinsonrsquos diseaserdquo in Proceedings of the 9th IEEEInternational Symposium on Medical Measurements and Appli-cations IEEE MeMeA 2014 Portugal June 2014

[15] C R Pereira D R Pereira F A D Silva et al ldquoA steptowards the automated diagnosis of parkinsonrsquos disease Ana-lyzing handwritingmovementsrdquo in Proceedings of the 28th IEEEInternational Symposium on Computer-Based Medical SystemsCBMS 2015 pp 171ndash176 Brazil June 2015

[16] P Drotar J Mekyska I Rektorova L Masarova Z Smekal andM Faundez-Zanuy ldquoAnalysis of in-air movement in handwrit-ing A novel marker for Parkinsonrsquos diseaserdquo ComputerMethodsand Programs in Biomedicine vol 117 no 3 pp 405ndash411 2014

[17] P Drotar J Mekyska Z Smekal I Rektorova L MasarovaandM Faundez-Zanuy ldquoContribution of different handwritingmodalities to differential diagnosis of Parkinsonrsquos Diseaserdquoin Proceedings of the 2015 IEEE International Symposium onMedical Measurements and Applications MeMeA 2015 pp 344ndash348 Italy May 2015

[18] C Taleb M Khachab C Mokbel and L Likforman-SulemldquoFeature selection for an improved Parkinsonrsquos disease identi-fication based on handwritingrdquo in Proceedings of the 2017 1stInternational Workshop on Arabic Script Analysis and Recogni-tion (ASAR) pp 52ndash56 Nancy France April 2017

[19] Z Smekal J Mekyska I Rektorova and M Faundez-ZanuyldquoAnalysis of neurological disorders based on digital processingof speech and handwritten textrdquo in Proceedings of the 2013International Symposium on Signals Circuits and Systems ISSCS2013 Romania July 2013

[20] D Surangsrirat A Intarapanich C Thanawattano R Bhi-dayasiri S Petchrutchatachart and C Anan ldquoTremor assess-ment using spiral analysis in time-frequency domainrdquo inProceedings of the IEEE SoutheastCon 2013MovingAmerica intothe Future USA April 2013

[21] STheodoridis andK Koutroumbas Pattern Recognition fourthedition Edition Academic Press fourth edition Edition 2009

[22] J P Papa A X Falcao and C T N Suzuki ldquoSupervised patternclassification based on optimum-path forestrdquo InternationalJournal of Imaging Systems and Technology vol 19 no 2 pp120ndash131 2009

[23] J P Papa C T N Suzuki and A X Falcao A library forthe design of optimum-path forest classifiers 2014 httpwwwicunicampbrafalcaolibopfindexhtmlLibopf

[24] V N Vapnik Statistical Learning Theory John Wiley Sons Inc2009

[25] G L Bezerra Ramalho D S Ferreira P P Reboucas Filhoand F N S de Medeiros ldquoRotation-invariant feature extractionusing a structural co-occurrence matrixrdquoMeasurement vol 94pp 406ndash415 2016

[26] S L Gomes E D S Reboucas E C Neto et al ldquoEmbeddedreal-time speed limit sign recognition using image processingand machine learning techniquesrdquo Neural Computing andApplications vol 28 pp 573ndash584 2017

[27] P P R Filho A C D S Barros G L B Ramalho et alldquoAutomated recognition of lung diseases in CT images basedon the optimum-path forest classifierrdquo Neural Computing andApplications pp 1ndash14 2017

[28] P P Reboucas Filho E D S Reboucas L B Marinho R MSarmento J M R S Tavares and V H C de AlbuquerqueldquoAnalysis of human tissue densities A new approach to extractfeatures from medical imagesrdquo Pattern Recognition Letters vol94 pp 211ndash218 2017

[29] P P Reboucas Filho R M Sarmento G B Holanda and D deAlencar Lima ldquoNew approach to detect and classify stroke inskull CT images via analysis of brain tissue densitiesrdquoComputerMethods and Programs in Biomedicine vol 148 pp 27ndash43 2017

[30] E C Neto S L Gomes P P Reboucas Filho and V H CDe Albuquerque ldquoBrazilian vehicle identification using a newembedded plate recognition systemrdquoMeasurement vol 70 pp36ndash46 2015

[31] L B Marinho J S Almeida J W M Souza V H CAlbuquerque and P P Reboucas Filho ldquoA novel mobile robotlocalization approach based on topological maps using classi-fication with reject option in omnidirectional imagesrdquo ExpertSystems with Applications vol 72 pp 1ndash17 2017

[32] P P Reboucas Filho J C D Santos F N C Freitas et alldquoNew approach to evaluate a non-grain oriented electricalsteel electromagnetic performance using photomicrographicanalysis via digital image processingrdquo Journal of MaterialsResearch and Technology 2017

8 Computational Intelligence and Neuroscience

IEEE Journal of Biomedical and Health Informatics vol 21 no2 pp 488ndash495 2017

[11] A-M Schiffer A J Nevado-Holgado A Johnen A RSchonberger G R Fink and R I Schubotz ldquoIntact actionsegmentation in Parkinsonrsquos disease Hypothesis testing usinga novel computational approachrdquoNeuropsychologia vol 78 pp29ndash40 2015

[12] R Guzman-Cabrera M Gomez-Sarabia M Torres-CisnerosM A Escobar-Acevedo and J R Guzman-Sepulveda ldquoParkin-sonrsquos disease Improved diagnosis using image processingrdquo inProceedings of the 2017 Photonics North (PN) pp 1-1 OttawaON Canada June 2017

[13] C R Pereira D R Pereira F A Silva et al ldquoA new computervision-based approach to aid the diagnosis of Parkinsonrsquosdiseaserdquo Computer Methods and Programs in Biomedicine vol136 pp 79ndash88 2016

[14] R Graca R S E Castro and J Cevada ldquoParkdetect Earlydiagnosing parkinsonrsquos diseaserdquo in Proceedings of the 9th IEEEInternational Symposium on Medical Measurements and Appli-cations IEEE MeMeA 2014 Portugal June 2014

[15] C R Pereira D R Pereira F A D Silva et al ldquoA steptowards the automated diagnosis of parkinsonrsquos disease Ana-lyzing handwritingmovementsrdquo in Proceedings of the 28th IEEEInternational Symposium on Computer-Based Medical SystemsCBMS 2015 pp 171ndash176 Brazil June 2015

[16] P Drotar J Mekyska I Rektorova L Masarova Z Smekal andM Faundez-Zanuy ldquoAnalysis of in-air movement in handwrit-ing A novel marker for Parkinsonrsquos diseaserdquo ComputerMethodsand Programs in Biomedicine vol 117 no 3 pp 405ndash411 2014

[17] P Drotar J Mekyska Z Smekal I Rektorova L MasarovaandM Faundez-Zanuy ldquoContribution of different handwritingmodalities to differential diagnosis of Parkinsonrsquos Diseaserdquoin Proceedings of the 2015 IEEE International Symposium onMedical Measurements and Applications MeMeA 2015 pp 344ndash348 Italy May 2015

[18] C Taleb M Khachab C Mokbel and L Likforman-SulemldquoFeature selection for an improved Parkinsonrsquos disease identi-fication based on handwritingrdquo in Proceedings of the 2017 1stInternational Workshop on Arabic Script Analysis and Recogni-tion (ASAR) pp 52ndash56 Nancy France April 2017

[19] Z Smekal J Mekyska I Rektorova and M Faundez-ZanuyldquoAnalysis of neurological disorders based on digital processingof speech and handwritten textrdquo in Proceedings of the 2013International Symposium on Signals Circuits and Systems ISSCS2013 Romania July 2013

[20] D Surangsrirat A Intarapanich C Thanawattano R Bhi-dayasiri S Petchrutchatachart and C Anan ldquoTremor assess-ment using spiral analysis in time-frequency domainrdquo inProceedings of the IEEE SoutheastCon 2013MovingAmerica intothe Future USA April 2013

[21] STheodoridis andK Koutroumbas Pattern Recognition fourthedition Edition Academic Press fourth edition Edition 2009

[22] J P Papa A X Falcao and C T N Suzuki ldquoSupervised patternclassification based on optimum-path forestrdquo InternationalJournal of Imaging Systems and Technology vol 19 no 2 pp120ndash131 2009

[23] J P Papa C T N Suzuki and A X Falcao A library forthe design of optimum-path forest classifiers 2014 httpwwwicunicampbrafalcaolibopfindexhtmlLibopf

[24] V N Vapnik Statistical Learning Theory John Wiley Sons Inc2009

[25] G L Bezerra Ramalho D S Ferreira P P Reboucas Filhoand F N S de Medeiros ldquoRotation-invariant feature extractionusing a structural co-occurrence matrixrdquoMeasurement vol 94pp 406ndash415 2016

[26] S L Gomes E D S Reboucas E C Neto et al ldquoEmbeddedreal-time speed limit sign recognition using image processingand machine learning techniquesrdquo Neural Computing andApplications vol 28 pp 573ndash584 2017

[27] P P R Filho A C D S Barros G L B Ramalho et alldquoAutomated recognition of lung diseases in CT images basedon the optimum-path forest classifierrdquo Neural Computing andApplications pp 1ndash14 2017

[28] P P Reboucas Filho E D S Reboucas L B Marinho R MSarmento J M R S Tavares and V H C de AlbuquerqueldquoAnalysis of human tissue densities A new approach to extractfeatures from medical imagesrdquo Pattern Recognition Letters vol94 pp 211ndash218 2017

[29] P P Reboucas Filho R M Sarmento G B Holanda and D deAlencar Lima ldquoNew approach to detect and classify stroke inskull CT images via analysis of brain tissue densitiesrdquoComputerMethods and Programs in Biomedicine vol 148 pp 27ndash43 2017

[30] E C Neto S L Gomes P P Reboucas Filho and V H CDe Albuquerque ldquoBrazilian vehicle identification using a newembedded plate recognition systemrdquoMeasurement vol 70 pp36ndash46 2015

[31] L B Marinho J S Almeida J W M Souza V H CAlbuquerque and P P Reboucas Filho ldquoA novel mobile robotlocalization approach based on topological maps using classi-fication with reject option in omnidirectional imagesrdquo ExpertSystems with Applications vol 72 pp 1ndash17 2017

[32] P P Reboucas Filho J C D Santos F N C Freitas et alldquoNew approach to evaluate a non-grain oriented electricalsteel electromagnetic performance using photomicrographicanalysis via digital image processingrdquo Journal of MaterialsResearch and Technology 2017

Computer Games Technology

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