Accurate and Reliable 3-lead to 12-lead ECGReconstruction Methodology for Remote Health

Monitoring Applications

Sidharth MaheshwariElectronics and Electrical Engineering

Indian Institute of Technology,Guwahati, India.

Email: sidharth.iitg@gmail.com

Amit Acharyya∗, P RajalakshmiElectrical Engineering

Indian Institute of Technology,Hyderabad, India.

Email: {amit acharyya, raji}@iith.ac.in

Paolo Emilio Puddu, Michele SchiaritiCardiovascular Sciences

Sapienza University of Rome, ItalyEmail:michele.schiariti@uniroma1.it,

paoloemilio.puddu@uniroma1.it

Abstract—Standard 12-lead (S12) system and Mason-Likar12-lead (ML12) system despite of being most acceptable systemsfor clinical usage are not the preferred lead systems for remotemonitoring (RM) applications. Usually RM applications involvewireless transmission of signals and a 2-3 lead system is preferredfor bandwidth and storage limitations and data transmissiontime. Generally, ECG compression techniques are applied for thesame, however, compression ratio (CR) depends on the number ofchannels and decreases with the increase in number of channels.Thus, it facilitates the usage of a 2-3 lead system. However, areduced lead (RL) system with 2-3 leads may be inadequate forthe information desired by the cardiologists who are accustomedto S12 or ML12 system pertaining to its decades old usage. Inthis paper, we attempt to provide solution to both technical andnon-technical limitations of RM applications. We reconstruct S12and ML12 systems from Reduced 3-lead (R3L) system comprisingof basis leads I, II, V2 using personalized or patient-specifictransformation. Two separate investigations have been carriedout for S12 and ML12 with their corresponding R3L systemscomprising of their respective basis leads. PhysioNet PTBDB andINCARTDB after wavelet based preprocessing were used in thisinvestigation. R2 statistics, correlation (rx) and regression (bx)coefficients were used to evaluate reconstructed signal againstthe original signal and the mean values obtained were 96.53%,0.982 and 0.968 (S12) and 96.53%, 0.982 and 0.968 (ML12)respectively. R3L system reduces number of leads and electrodesfrom 12 and 10 to 3 and 5 respectively, lowers bandwidth andstorage requirements, data transmission time and increases CR.The study shows that basis leads obtained from S12 outperformsthe basis leads of ML12 for reconstruction of precordial leads.

I. INTRODUCTION

In this paper we propose an accurate and reliable leadreconstruction methodology for reconstruction of missing pre-cordial leads of standard 12-lead (S12) and Mason-Likar 12-lead (ML12) system from a Reduced 3-lead (R3L) system andpresent our preliminary research findings. The basis lead setfor R3L system are leads I, II and V2 and the target leads areV1, V3, V4, V5 and V6 for both S12 and ML12 system. Waveletbased preprocessing, Heart-Vector projection theory and leastsquare (LS) fit method have been employed to reconstruct leadswith high diagnostic quality.

This work is supported by the DIT, India under the Cyber PhysicalSystems Innovation Hub under Grant number: 13(6)/2010−CC&BT , dated01.03.11

Cardiovascular diseases (CVD) are one of the prime causesof human mortality today [1]. Detection and prevention ofCVD is therefore an important issue. Research is going onthroughout the world to detect CVD comfortably and remotelyusing mobile devices for ambulatory and remote healthcareservices thus alleviating the need of physical presence of thepatient in the hospital. S12 system is most frequently usedin clinical practice [2] and pertaining to its decade old usagecardiologists are accustomed to it. For long-term continuousmonitoring, exercise tests and emergency ECG Mason-Likar12-lead (ML12) ECG system [3] is considered to be mostacceptable substitute for the S12 system [2]. However, boththe systems involve 10 electrodes, which is cumbersome anduncomfortable, and 8 independent channels which inhibitstheir application in Remote Health Monitoring (RM). RMmonitoring is used for comfort of the patients e.g. homemonitoring [4] as well as for upliftment of healthcare facilitiesin remote and rural areas which lack in facilities and skilledmedical practitioners [5]. RM scenarios often involve wirelesstransmission of signals to nearby state-of-the-art health carefacilities, the major bottlenecks encountered are bandwidth,storage and data transmission time [6,7]. Generally, ECGsignal compression techniques are applied to overcome theaforementioned limitations, however, compression ratios (CR)depend on the number of channels being used. CR is greaterfor less number of channels [8] and hence, a 2-3 lead systemis preferred for remote monitoring applications.S12 and ML12 are the most acceptable lead systems forclinical usage, however, they are accompanied by severalconstraints as discussed earlier and hence, their usage isavoided in remote monitoring applications. In this paper, weattempt to address the aforementioned limitations using leadreconstruction technique and propose an accurate and reliablepersonalized lead reconstruction methodology for reconstruct-ing the missing precordial leads of both S12 and ML12 systemsfrom a Reduced 3-Lead (R3L) system comprising of I, IIand V2 as the basis leads. The usage of a R3L system andpersonalized lead reconstruction technique will lead to thefollowing: First, Number of electrodes and leads are reducedto 5 and 3 respectively, second, CR increases as the numberof channels decrease and third, Availability of S12 and ML12leads with high diagnostic quality (see section IV).This paper is organized in the following manner: section

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II presents the background and previous work, section IIIpresents the methodology and the databases used for eval-uation, section IV presents results and discussions and weconclude the paper in section V.

II. BACKGROUND AND PREVIOUS WORK

Among the existing literature Dower was the first to pro-pose a transformation matrix, known as Dower Transform(DT),to transform Frank vectorcardiographic system to S12 system[9]. Recently in [10] transformation matrix was obtained usingLeast-square (LS) fit method by applying it on a populationof patients(known as population based transformation matrix)has been shown to outperform DT. EASI system to S12system is another transformation proposed which uses LSfit method or DT [11]. [12] posits that the transformationinvolving reconstruction of missing precordial leads of S12outperforms the transformation of EASI system to S12 system.There have been several investigations involving reconstructionof missing precordial leads [13-19], however, investigation ontransformation of 3-lead subset to S12 system are very few andmost of the works have focused on the transformation of 4-lead subset to S12 using either patient-specific or populationspecific coefficients. [20] has investigated into the transfor-mation of ML12 to S12 system. [21] has investigated intothe transformation of reduced 4-lead systems comprising ofbasis leads derived from ML12 system to the missing leads ofML12, S12 and 18-lead ECGs. To the best of our knowledgewe haven’t found any work which has investigated into thetransformation of R3L system to ML12 system where theR3L system is composed of a subset of leads (I, II andV2) from ML12 system. In this paper, we investigate thereconstruction of missing precordial leads of both S12 andML12 systems independently using our proposed methodologywhich includes a well defined preprocessing module. In all theprevious works personalized or patient-specific transformationshave outperformed population specific transformations andhence, we have used personalized transformations (PT) in thisinvestigation.

Fig. 1. Proposed methodology in the envisaged remote health monitoringenvironment

III. METHOD AND MATERIAL

We envisage a scenario in which patients are registeredin a state-of-the-art health center which maintains their healthrecord and the acquired ECG is transmitted to the health centerfrom a remote/rural or local area for diagnosis, storage and up-date. The complete process involves acquisition, preprocessing,coefficient generation, transmission and lead reconstruction.

Here, we focus on the preprocessing, coefficient generationand reconstruction of missing precordial leads of both S12and ML12 leads separately. Fig. 1 shows the summary of themethodology followed. The raw ECG signal is passed througha wavelet based preprocessing module to remove baselinewandering and noise. This preprocessed signal is then usedfor generation of personalized coefficients using Heart-VectorProjection (HVP) theory and Least-Square (LS) Fit method.The R3L system along with the transformation coefficientsare then used to reconstruct the missing precordial leads usingHVP theory. Finally, we evaluate the reconstruction resultsusing R2 statistics, correlation (rx) and regression coefficients(bx).

A. Material

PhysioNet’s PTBDB and INCARTDB [21,22], sampled atfrequencies 1 kHz and 257 Hz respectively, have been usedin this investigation. First recording of 277 patients fromPTBDB and 71 recordings of 32 patients from INCARTDBwere considered for this study. 13 patients from PTBDBand 4 recordings from INCARTDB were excluded from thestudy due to their extreme artifacts. Patients in PTBDB weredivided in Healthy control - 51(HC) and Unhealthy - 226(UH)subjects and simulations were performed separately for both.INCARTDB was not categorized as it has only the unhealthysubjects and independent simulations were performed for it. Itshould be noted that INCARTDB consists lengthy 30 minutesrecordings while PTBDB consists of less than 2 minutesrecordings.

B. Preprocessing Module

Here, we propose to introduce the preprocessing modulecomprising of baseline wandering (BW) removal based ondiscrete wavelet transform (DWT) [23] and denoising based ontranslation invariant wavelet transform (TIWT) [24,25].Figure2 provides the snippet of the MATLAB code for implemen-tation of the preprocessing module. Other denoising methods[26,27] can be used, however, TIWT appears to outperformthe rest and therefore we used it in the proposed methodology.The implementation of TIWT requires the input number ofsamples to be in the power of 2. So, the database was denoisedin parts taking a number of samples at a time in the powerof 2 and attempted to accommodate as many samples aspossible. This recording after preprocessing was then used

[c,t] = wavedec(x,9,‘sym10’); %Decomposition of signalsignal = wrcoef(‘a’,c,t,‘sym10’,9); %Reconstruction of signalfrom the approx. coefficientsBW removed signal = x − signal; %Subtracting the BW fromoriginal signalqmf = MakeONFilter(‘Symmlet’,8);denoised signal = recTI(BW removed signal,‘H’,qmf);

Fig. 2. Snippet of MATLAB code for implementation of preprocessingmodule.

for all further processing throughout the work. For baselinewandering the level of decomposition was down to level 9(PTBDB) as the sampling frequency of PTBDB is 1 kHz and7 (INCARTDB) as sampling frequency of INCARTDB is 257Hz and wavelet used was Symmlet 10. For denoising the level

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of decomposition was self determined by the code, waveletused was symmlet 8 and hard thresholding was used.

C. Coefficient Generation using HVP theory and LS fit Method

[28] states that heart can be approximated as a single dipolevector fixed in 3−D space and the orientation and magnitudeof this vector varies during a cardiac cycle. The potentialgenerated on any point of the body (1) is the projection ofthis heart-vector (

−→H ) on the vector pointing at that point from

the middle of the heart-vector (Lead vector,−→L ).

V =−→H · −→L = aX + bY + cZ (1)

Where−→H = Xi + Yj + Zk and

−→L = ai + bj + ck. In (1), X, Y

and Z can be replaced by any other set of leads exploiting thelinear simplistic model of the heart. In this work we have usedleads I, II and V2. The components of vector

−→L are known

as the transformation coefficients and can be generated usingLS fit method. When these coefficients are generated from theECG of a particular patient, they are known as personalized orpatient-specific and when obtained from a population, they areknown as population specific coefficients. Equation 2 providesthe solution of LS fit method when applied on (1).[ ai

bici

]=

[ΣI2 ΣI · II ΣI · V2

ΣI · II ΣII2 ΣII · V2

ΣI · V2 ΣII · V2 ΣV 22

]−1 [ΣV · IΣV · IIΣV · V2

](2)

Training set of first 5000 points from the recording was usedfor coefficient generation and rest of the recording servedas testing set, thus, keeping the training and the testing setsdisjoint. The complete work was carried out on MATLAB(Version 7.10.0.499 R2010a).

D. Performance Evaluation Metric

R2 statistics, correlation and regression coefficient [10,29]have been used as performance evaluation metric. R2 statisticshas been used to evaluate the degree of association between themeasured and the reconstructed signal. Perfect retracing of themeasured wave by the reconstructed wave will be indicated bya value 100%. Correlation coefficient (rx) [29] is a good metricto estimate the similarity between two signals and regression(bx) [29] fairly estimates the amplitude differences betweenthe measured and reconstructed signal. All signals were meancentered before applying the evaluation metrics.

R2 ={1− Σ[Derived(sample k)−Measured(sample k)]2

Σ[Measured(sample k)]2

}100(3)

rx =

{Σ(Measured Sample i)×(Derived Sample i)

(Σ(Measured Sample i)2×Σ(Derived Sample i)2)12

}(4)

bx ={

Σ(Measured Sample i)×(Derived Sample i)(Σ(Measured Sample i)2

}(5)

TABLE I. LEAD-WISE MEAN R2 , CORRELATION (rx) ANDREGRESSION (bx) VALUES OF HEALTHY CONTROL (HC) AND UNHEALTHY

(UH) PATIENTS IN PTBDB AND RECORDINGS IN INCARTDB

PTBDB INCARTDBHC UH

R2 rx bx R2 rx bx R2 rx bx

V1 94.69 0.973 0.952 92.92 0.963 0.939 86.32 0.935 0.923

V3 96.52 0.983 0.973 94.67 0.973 0.956 87.32 0.931 0.915

V4 91.83 0.959 0.939 86.33 0.928 0.889 81.03 0.898 0.876

V5 93.53 0.968 0.957 86.04 0.927 0.891 83.42 0.919 0.911

V6 94.69 0.973 0.967 88.06 0.936 0.908 78.22 0.893 0.872

Avg 94.25 0.971 0.958 89.60 0.945 0.916 83.26 0.915 0.899

Min 91.83 0.959 0.939 86.04 0.927 0.889 78.22 0.893 0.872

TABLE II. MEAN VALUES OF PERSONALIZED TRANSFORMATIONCOEFFICIENTS OBTAINED.

PTBDB INCARTDBHC UH

V1 -0.6767 -0.0561 0.4926 -0.6250 -0.0965 0.5006 -0.7486 -0.1439 0.5544

V3 0.7297 0.3298 0.8506 0.4449 0.3588 0.9637 -0.1710 0.4957 1.0684

V4 1.4101 0.5072 0.4149 0.9036 0.5792 0.5915 0.3125 0.7231 0.4713

V5 1.5883 0.4610 0.0314 1.0042 0.5933 0.1774 0.7948 0.7262 -0.0668

V6 1.1729 0.3618 -0.1173 0.7330 0.4710 -0.0442 0.7091 0.6404 -0.3097

IV. RESULTS AND DISCUSSIONS

Table I shows the mean values of R2 statistics, correlation(rx) and regression (bx) coefficients for HC and UH subjects inPTBDB and all the recordings of INCARTDB. The proximityeffect stated in [19] can be observed from the table. Leads inclose proximity of the basis lead have better reconstructioncompared to those away from it, this is evident from theR2 values of leads V1 and V3 which are higher than otherprecordial leads owing to their close proximity to V2, thechosen basis lead in this investigation. For S12 system, theminimum R2 values are obtained for lead V4 while for ML12system its for lead V6. It can also been seen from Table Ithat the R3L system consisting of basis leads obtained fromS12 system outperforms ML12 system for the reconstructionof missing precordial leads. It should be noted that only themissing precordial leads have been discussed as other leads canbe obtained from the basis lead set using the linear dependenceof III and augmented leads on leads I and II.

To provide an insight into the corresponding relationshipbetween R2 values and the quality of reconstruction obtainedwe present case study of six different patients. Fig. 3A and3B correspond to patients with mean R2 values of 94.28%and 90.06% respectively (mean taken over 5 reconstructedmissing precordial leads) which are approximately equal to themean values given in Table I. Fig. 3C is a similar mean casesubject from INCARTDB with mean R2 value being 84.97%.Similarly, Fig. 3D, 3E and 3F provide the reconstruction resultof subjects with minimum mean R2 values from HC, UHand INCARTDB respectively. The range of R2 values forreconstructed leads shown in Fig. 3 is from 5.512% to 99.52%.It should be noted that R2 values of 80% and above can beconsidered to have significant diagnostic value and R2 values

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Fig. 3. Comparison between original (blue) and derived (red) leads of the subjects from PTBDB and INCARTDB. Sub-figures A and B show the comparisonof subjects from HC and UH category respectively with the mean R2 values near to actual means provided in Table I. C is the corresponding mean casefrom INCARTDB. Sub-figures D, E and F correspond to minimum case of mean R2 values for the subjects from HC, UH and INCARTDB respectively. Thecorresponding R2 values of the reconstructed lead has been provided with the figure.

Fig. 4. Mean () and range (whiskers) of the personalized 5×3 = 15 transformation coefficients.

of 90% and above can be considered to be an accurate retraceof the original signal for all practical purposes. In HC category,a total of 255 (51×5) leads were reconstructed out of which96.08% leads had more than 80% R2 values and 88.6% leadshad more than 90% R2 value. In UH category, a total of1130 (226×5) leads were reconstructed out of which 85.75%patients had more than 80% (51×5) value and 66% leads hadmore than 90% R2 value. For the reconstruction of 355 (71×5)precordial leads of ML12 system about 73.24% had more than80% R2 value and 49% leads had more than 90% R2 value.

Table II provides the mean values of personalized transfor-mation coefficients for PTBDB (HC and UH) and INCARTDB.Fig. 4 plots the mean and range of the personalized transfor-

mations coefficients of five missing precordial leads (5×3 =15) resulting in total of 15 coefficients. The transformationcoefficients depend on the following: position of electrodes,age, sex, size, shape, body fat distribution, homogeneity andseveral cardiac disorder faced by the patient [19]. Hence, ageneral set of coefficients cannot account for all of the afore-mentioned parameters. Thus, personalized coefficients resultin increased performance over population based coefficientsas they can seen to vary significantly from their mean valuesin Fig. 4.

Reconstruction of missing precordial leads offers severaladvantages in the context of remote healthcare for CVD. Withthe usage of R3L system the number of channel reduces, thus,

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increasing the compression ratio and furthermore, reducingthe storage requirements and facilitating wireless transmission.The usage of this reduced lead system does not require anyspecial ECG machine or any significant change in alreadyexisting system. Lead III and the augmented leads along withthe basis leads are readily available. No specific or furthertraining is required for the application of this system, further-more, this system requires placement of only one precordiallead which will ease and speedup the overall process of ECGacquisition. With R3L system there is a reduction in numberof electrodes from 10 to 5 compared to that of S12 and ML12systems, thereby, reducing tangling of wires and inconveniencecaused to both patients and caregivers. It has been foundthat certain cardiac disorders are specifically indicated by aparticular precordial lead [20]. This system provides flexibilityof choosing any of the six precordial leads to form the reducedlead system. The reconstruction of the missing precordialleads depends on the generation of personalized transformationcoefficients which can be reused over time as demonstrated byGregg et al [21].

V. CONCLUSION

In this work, we have proposed a methodology to recon-struct missing precordial leads of S12 and ML12 leads reliablyand accurately using a wavelet based preprocessing module.We have shown that the basis leads obtained from Standard 12-lead (S12) system outperforms the basis leads obtained fromMason-Likar 12-lead (ML12) system. The former performsbetter for all the precordial leads compared to the latter. Basisleads obtained from ML12 system also fails to reconstructleads V5 and V6 w.r.t other precordial leads when comparedwith results obtained from basis leads of S12 system. Asthe results indicate, personalized transformation is capable ofreconstructing leads with high diagnostic value, practicallyretracing the original signal for diagnosis purposes. Hardwaresystem implementation based on the proposed lead reconstruc-tion methodology and real-life field deployment form part ofour future research. We believe the proposed methodology andthe corresponding hardware will be of extreme importance toprovide accurate and reliable diagnosis, prognosis and medicalsolutions to the CVD patients across the globe especially in theremote and rural areas of the developing and under-developedcountries exploiting the cyber-physical systems and internet ofthings based emerging technologies.

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