High-Frequency Oscillations Detected in Epileptic Networks Using

Swarmed Neural-Network Features

HIRAM FIRPI ,1 OTIS SMART,1 GREG WORRELL,2 ERIC MARSH,3 DENNIS DLUGOS,3 and BRIAN LITT4,5

1Department of Electrical and Computer Engineering, Georgia Institute of Technology, 777 Atlantic Dr., Atlanta, GA30332-0250, USA; 2Department of Neurology, Mayo Clinic, Rochester, MN 55901, USA; 3Division of Neurology,

The Children�s Hospital of Philadelphia, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA4Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA; and 5Department of Bioengineering,

University of Pennsylvania, Philadelphia, PA 19104, USA

(Received 15 July 2006; accepted 18 May 2007)

Abstract—Localizing epileptic networks is a central challengein guiding epilepsy surgery, deploying antiepileptic devices,and elucidating mechanisms underlying seizure generation.Recent work from our group and others suggests that high-frequency epileptic oscillations (HFEOs) arise from brainregions constituting epileptic networks, and may be impor-tant to seizure generation. HFEOs are brief 50–500 Hzpathologic events measured in intracranial field and unitrecordings in patients with refractory epilepsy. They arechallenging to detect due to low signal to noise ratio, andbecause they occur in multiple channels with great frequency.Their morphology is also variable and changes with distancefrom intracranial electrode contacts, which are sparselyplaced for patient safety. Thus reliable, automated methodsto detect HFEOs are required to localize and track seizuregeneration in epileptic networks. We present a novel methodfor mapping the temporal evolution of these oscillations inhuman epileptic networks. The technique combines a particleswarm optimization algorithm with a neural network tocreate features that robustly detect and track HFEOs inhuman intracranial EEG (IEEG) recordings. We demon-strate the algorithm�s performance on IEEG data from sixpatients, one pediatric and five adult, and compare it to anexisting method for detecting high-frequency oscillations.

Keywords—High-frequency epileptiform oscillation (HFEO),

Detection, Feature extraction, Particle swarm optimization,

Neural networks, Epilepsy, Intracranial EEG.

INTRODUCTION

Recently, the existence of high-end EEG recordingequipment has allowed researchers to study oscilla-tions in bandwidths outside of the elemental Bergerfrequency ranges.1 High-frequency oscillations have

been linked to various natural neurological (physio-logical) processes: the 40–60 Hz band has been relatedto the learning and memory processes, 100–200 Hz tomemory consolidation, and 600 Hz to somatosensoryoscillations (i.e., relating to perception of sensorystimuli from skin or internal organs). Additionally,from a pathological perspective, recent studies havepresented evidence linking the high-frequency oscilla-tions to the localization, generation, and propagationof epileptic seizures.11

Studying oscillations from 80 to 500 Hz in the hip-pocampal and entorhinal cortical areas of rodents andhumans, Staba et al. revealed evidence for the exis-tence of high-frequency oscillations, termed fast rip-ples, which possess a strong association with regions ofseizure initiation.18 Similarly, Worrell et al.19 foundoscillations within the 60–100 Hz band, called high-frequency epileptiform oscillations (HFEO�s), whichappear to localize the seizure onset zone of six patientswith neocortical epilepsy. Furthermore, Jirsch et al.7

studied seizure generation and high-frequency oscilla-tions (HFO�s) ranging from 100 to 500 Hz recordedwith depth macroelectrodes discovering distinct high-frequency oscillations localized in areas of seizureonset and propagation in several patients reflecting thepartial synchronization of HFO�s previously studiedwith microelectrodes.2,19

In sum, there exists a frequency spectrum (seeFig. 1) of oscillations within the EEG that had untilrecently been largely ignored, but in recent studies hasbeen demonstrated to localize epileptogenic brain.Although all high-frequency oscillations may becharacterized as brief bursts lasting milliseconds to afew seconds with low signal-to-noise ratio, only twoHFOs thus far appear pathological: fast ripples andhigh-frequency epileptiform oscillations. Because fastripples and HFEOs tend to spatially localize networks

Address correspondence to Hiram Firpi, Department of Electri-

cal and Computer Engineering, Georgia Institute of Technology, 777

Atlantic Dr., Atlanta, GA 30332-0250, USA. Electronic mail: hfirpi

@ieee.org

Annals of Biomedical Engineering (� 2007)

DOI: 10.1007/s10439-007-9333-7

� 2007 Biomedical Engineering Society

of epileptogenic circuits and contribute in somefashion—albeit unclear—to ictogenesis, great interesthad been taken to detect and analyze these events.Although literature emphasizes the importance ofautomatic detection of epileptic high-frequency oscil-lations,4,7 only two groups have attempted to auto-matically detect epileptic high-frequency oscillations:Smart et al.,17 and Staba et al.18 Smart et al. usednon-linear energy and curve length features in thefeature extraction stage followed by a fuzzy c-meansclustering, an unsupervised technique. In work byStaba et al., a root mean square feature was usedcascaded with a threshold as a detector. The thresh-old was set to flag only those HFOs that lay two tothree standard deviations from the mean, potentiallymissing events. Although these efforts presentedpromise in automatic detection of HFOs, the methodswere not sound in the feature selection or creationstage, since each approach simply selected featuresthat rely on the limited understanding of the spectralproperties of HFOs, which are not completelyunderstood.

Previous work does not emphasize the importanceof the quality of features in a pattern classificationsystem, yet feature extraction is arguably the mostimportant stage,10 providing the most flexibility inorder to yield relevant data for the classifier. Manyclassical feature extraction methods are found in theliterature; from selecting features by trial-and-error tousing a genetic algorithm to select optimal featuresaccording to a fitness function. However, the featuresused typically are conventional measures that mightnot capture all the information available in the data ofinterest due to being designed in a general framework,that is, they were not designed to consider aspects orproperties of a signal in particular. For instance, en-ergy as a feature can be evaluated no matter the sourceof the signal (e.g., biological, mechanic, and acoustic).As the complexity of a problem increases, the knowl-edge of the mechanisms generating the signals oftendecreases, and traditional methods or features mightfall short for the aforementioned reason. Thus, there isan urgent need for algorithms that design data-drivenfeatures sensitive to particular anomalies and discard

noise. However, these features might not have aphysical meaning as classical features do, but ratherthey are computer-crafted measures that explode anysignature associated with the different classes and thusassist the detector (i.e., classifier) in the HFEO detec-tion.5 For this reason, we term these kinds of measure:artificial features.

In this paper, we implemented a novel algorithmthat compounded a neural network (NN) and particleswarm optimization (PSO) algorithm to create optimalfeatures for class-separation between epileptic high-frequency oscillations and baseline (BS) activity. Ouralgorithm is implemented to derive artificial featuresfor two cases of neocortical epilepsy: a pediatric pa-tient with cortical dysplasia and five adult patients withnon-lesional extratemporal epilepsy. In the formercase, the patient was diagnosed as having corticaldysplasia,12 which is a disorder of neuronal migrationproducing architectural abnormalities in the cerebralcortex. In the latter case, the patients were diagnosedwith non-lesional, extratemporal neocortical epilepsythat typically spread to multiple contiguous regions.These two cases typified most cases of persons withneocortical epilepsy in that their seizures were resistantto medication therapy, were poorly localized, explo-sive, and regional (i.e., not discretely localized) at theonset.

THEORETICAL BACKGROUND

Neural Networks

A NN6 is a computational architecture that is con-stituted by several nodes configured in interconnectedlayers. At each node all the incoming inputs are scaledusing weights, linearly combined, and processedthrough an activation function. The weights, whichinterconnect two nodes of the neural network (NN),are determined using one of the two methods oflearning algorithms: local learning and global learning.Local learning algorithms, such as the well-knownback-propagation method, are based on calculating agradient to find the optimal set of weights in a mini-mum-error sense. Unlike, global learning methods,

Berger bands

HFEOs(Slow Ripples)

Ripples

0 40

90

200

Fast Ripples

600

Very Fast Ripples

25050

100

FIGURE 1. The spectrum of high-frequency oscillations that have been investigated by several research groups along with apossible frequency band (>600 Hz) of very fast ripples, which may be defined in a near future.

FIRPI et al.

local learning algorithms often confine to a localminimum without ever reaching the global minimum.

Evolutionary algorithms (EAs) have been imple-mented to train neural networks as a global learningmethod. EAs return an optimal set of weights andavoid local minimums in a stochastic fashion by ran-domly initializing and updating a population of weightsaccording to a heuristic and an objective function.

Particle Swarm Optimization

PSO is a search optimization algorithm that wasdevised by Kennedy and Eberhart8 in the mid-1990s.The PSO is inspired by a different concept than that ofevolutionary algorithms (i.e., survival-of-fitness), butwith a similar outcome algorithm, which has beenproven to be a good search algorithm achieving similarresults to those of their genetic counterparts.

The PSO uses a population of potential solutions,called a swarm of particles, that traverses a searchspace of fitness values for an optimal value accordingto a particle leader—the best solution among all par-ticles of the current swarm, which exchanges infor-mation with other particles such that each particleconverges to a new optima in the search space alongwith the global leader.

More specifically, each particle in PSO has a currentposition, denoted xi, a velocity (rate of positionchange), denoted vi and a personal best, yi. This yi isthe best (the one with best fitness) solution found byparticle i. In other words, yi contains the best previousposition that xi has reached at current time t. Theequation that manipulates the velocity is called thevelocity update equation and is stated in (1).

viðtþ1Þ ¼wviðtÞþ c1r1ðtÞðyi�xiðtÞÞþ c2r2ðtÞðyi�xiðtÞÞð1Þ

where the term vi(t + 1) is the velocity update forparticle i for time step t + 1. The coefficients c1 and c2are the acceleration coefficients, where the first con-stant moderates the maximum step size toward theglobal best particle whereas the second moderates thestep size toward the best personal position of thatparticle. The acceleration coefficients are bounded bythe inequality 0 < c1, c2 < 2. The values r1(t) and r2(t)are random values, with uniform distributionrðtÞ � Uð0; 1Þ, making the PSO algorithm stochastic.The inertia weight, w,15 is a factor used to control thebalance of the search algorithm between explorationand exploitation (i.e., from a global search to a localone). To reduce the probability that vi (t) escapes fromthe search space, limited by the range [)Xmax, Xmax],vi (t) is clamped by the range [)Vmax, Vmax]. Eachparticle updates its position using the followingexpression (2).

xiðtþ 1Þ ¼ xiðtÞ þ viðtþ 1Þ ð2Þ

In swarm terminology, the above equation meansthat the particle i is flying to its new position xi(t + 1).After the new position is calculated for each particle,the iteration counter increases t and the new particlepositions are evaluated. The algorithm�s evaluationcontinues until either a stop condition is met, denotingconvergence to a globally optimal particle, or themaximum number of generation is reached.

PSO-NN for Feature Creation

PSO-neural network algorithm (PSO-NN) is asupervised, hybrid (i.e., parametric and non-paramet-ric) algorithm that detects underlying patterns to sep-arate the HFEO events from the IEEG BS. This workuses a PSO algorithm to train a neural network. In apattern recognition system, NNs are typically em-ployed as classifiers, linking a target label to every classthat should be discriminated. Conventionally, the in-puts to the NN are classical features selected/extractedin the feature extraction stage. In this work, we use aNN as a feature extractor by (1) using the PSO algo-rithm as the learning algorithm and (2) redefining theobjective function. Using the PSO algorithm to trainthe NN ensures that a global learning algorithm maybe able to find the optimal set of weights and can avoidlocal minimums.9 Another advantage is its simplicity,making it fast to evaluate and reducing the computa-tion time during the training process.

The second point mentioned above deals with thefact that traditionally classification metrics entail theevaluation of a classifier to measure the performance ofa feature extraction strategy making the training pro-cess computationally intensive. To address this issue,we use the Fisher discriminant ratio (FDR) (discussedin further details later) as the cost function. This costfunction measures the intra-class distance betweenboth classes IEEG BS data and HFEO events. The bestNN-feature is the one that provides the maximum in-tra-class distance possible.

To create NN-based features from raw data, weinput to the NN the current and few past values(delays) of the processed IEEG signal (see Fig. 2).Mathematically speaking

y k½ � ¼ / xð Þ ð3Þ

where / is a NN

/ xð Þ ¼ f wNf wN�1 � � � f wl � � � f 1x� �� �� �� �

ð4Þ

w is the weight matrix for layer l, which values will befound by the PSO algorithm, f is the activation func-tion, and

HFEO-Detection Using PSO-NN Artificial Features

x ¼ xi xi�1 xi�2 � � � xi�ðm�1Þ� �

ð5Þ

is the set of delayed samples input to the NN. There-fore, the NN-based artificial feature is defined/ : Rm ! R, where m is the number of delays.

The fitness function scores the solutions (sets ofweights for the NN) and drives the PSO algorithmsearch through the weight (search) space. As previ-ously mentioned, a classifier can be placed at the out-put of the network (see Fig. 2) to calculate theclassification accuracy to score the candidate set ofweights (particle), however, it will be computationallyexpensive given that the classifier needs to be trainedon-line. To measure the goodness of the particles, theFDR that measures the separation of the probabilitydensity functions (PDFs) is utilized. The equation ispresented in (6).

FDR ¼ jlBS � lHFEOjffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðr2

BS þ r2HFEOÞ=2

q ð6Þ

where li is the mean of the class and ri2 is the variance

for the BS and HFEO classes. The FDR is computa-tionally inexpensive to evaluate, making the PSO-NNalgorithm faster than using a fitness function that in-cludes classification accuracy. By using this metric, theartificial features are also being detached from thestructure of the classifier. In other words, once the PSOalgorithm finds the weights, the specialized feature(s)can be input to any classifier (i.e., parametric or non-parametric) and a good classification accuracy may beachieved.

METHODOLOGY

IEEG Data Collection

IEEG signals were collected from six patients withepilepsy who underwent long-term continuous moni-toring for pre-surgical evaluation. Each patient gavetheir informed consent for participation in researchstudies under the approval of the Internal ReviewBoards at Emory University (EU), the University ofPennsylvania (UOP), and the Children�s Hospital ofPhiladelphia (CHOP) before subdural grids, strips,and depth electrodes were implanted according tostandard protocols for presurgical evaluation.4,13 Therecordings were collected using Ad-Tech (AD-TECHMedical Instrument Corporation, Racine, WI) elec-trodes and a 12-bit, 64-channel Nicolet BMS-5000(Nicolet Biomedical, Madison, WI) epilepsy moni-toring system for the EU and UOP patients and aGrass-Telefactor 12-bit, 128 channel system for theCHOP patient. The referentially recorded EEG wasdigitized at 200 samples per second and archived toCD-ROM for future signal processing after band-passfiltering (0.01–100 30–96 Hz and a ripple rate of 5%),notch filtering (60 Hz) to eliminate line noise, andremoving common mode artifact through bipolarelectrode montages.

Registering all interictal HFEOs across 64 channelsof EEG in the six patients was an impractical task,especially since the duration of the oscillations was onthe order of milliseconds. Therefore, two epileptolo-gists (G.A.W. and E.M.) annotated the HFEOs intwo subsets of records with short duration (at most

Neural Network

Particle Swarm

IEEG Signal

Delays

t

xi

xi-1

xi-(m-1)

Threshold or Classifier

x = Σ

Summation

Preprocessing

Processed IEEG Signal

t

d/dt

...

Preprocessing Feature Extraction Classification

w1

w2

FIGURE 2. Schematic of the PSO-NN approach to creating optimal features for classifying patterns. The preprocessing stageconstitutes a band-pass filter in cascade with a whitening filter (derivative operator) to process the original time-series for the EEG.Although this work applies PSO-NN to HFEOs, the scheme is general for any other high-frequency oscillation of interest.

FIRPI et al.

3.5 min) per patient after a preliminary trainingsession to mark only ‘‘gold standard’’ HFEOs, orepileptic oscillations on which events that character-istically three epileptologists (i.e., G.A.W., E.M., andB.L.) would agree. The first subset comprised thetraining data that supported the creation of artificialfeatures via PSO-NN and the classification of futurevalues in a testing set according to a reference ofvalues (in the case of a k-NN rule) or a thresholdderived from the training set. The second subsetcomprised the testing data that aided in evaluatingand statistically comparing the performance of eachmethod. In both sets, the data for each subject con-sisted of single-channel records that were constructedby randomly selecting and concatenating epilepticoscillations and non-epileptic activity (background)from multi-channel continuous IEEG—including butnot limited to the epileptic focus that was conjecturedby the epileptologists. Synchronized with each recordin the training or testing set was a vector of targets inwhich moments of epileptic and non-epileptic activitywere, respectively, labeled with a value of one andzero. The training data per subject was a single recordwith at least 40 epileptic oscillations complemented by1 min of background. The testing set per subjectconstituted 10 records with 30 epileptic oscillationssurrounded by (before and after) one second ofelectrographic background in each record. Figure 3illustrates a 20-s, multi-channel IEEG record that wasreviewed and randomly scored (underlining) by anepileptologists for constructing a training or testingrecord.

Pattern Recognition

Typical pattern classification systems consist of threemodules: preprocessing, feature extraction, and classi-fication. For this case, preprocessing is the stage wherethe signal-to-noise ratio is enhanced. The featureextraction stage highlights the relevant data—in thiscase, the HFO events—while ignoring the irrelevantdata. Finally, the classifier (detector) catalogs the featurevalues as BS signal or HFEO events. Figure 2 shows thecomponents and pathway of the PSO-NN algorithm.

Preprocessing

The IEEG time-series were processed by anenhancement stage to improve the signal-to-noise ratioof the HFEO events. According to Worrell et al.,19 theHFEO frequency range for adults diagnosed withneocortical epilepsy is between 60 and 100 Hz. On theother hand, the frequency range for pediatric cases iswithin 50–85 Hz. Figures 4a and 6a picture an IEEGsegment that contained marked HFEOs (indicatedwith solid circles) for a pediatric case and an adultcase, respectively. First, a notch filter at 60 Hz wasimplemented to remove line noise. We implemented forall subjects a Butterworth band-pass filter from 30 to96 Hz with negligible ripple in the pass-band and sharpcut-off frequencies at 30 and 96 Hz resulting in verynarrow transition bands of ~2 Hz. Possibilities ofphase distortion that may introduce time-delay wereremoved by (1) filtering the IEEG data, (2) filtering thereverse of the filtered data, and (3) later reversing theoutput of the last operation.

FIGURE 3. A 20-s, multi-channel EEG record illustrating HFEO trains throughout the entire channel. The solid lines (i.e., channelsG2 and G3) indicate the HFEO that are associated with the onset of the seizure whereas the dotted lines indicate The HFEO activityin the channels to which the seizure will spread out (i.e., G10 and G11).

HFEO-Detection Using PSO-NN Artificial Features

Feature Extraction

A sliding observation window is used to view IEEGdata. The sliding window is parameterized by thelength of the window, which is denoted L, and thedisplacement of the window, which is denoted D. Thelength L defines the number of points that will beevaluated at the same time before to move the windowby D, which is defined as the number of new pointsthat will be used in the next evaluation or D = L)O,where O is the overlap with the previous window. Toreduce the variability and highlight the trend in thesignal evolution all points within each window positionare averaged. Thus, recalling (3) and (5), and placingthe average operator, the resultant equation is

y½k� ¼ 1

L

XDðk�1ÞþL

i¼1þðk�1ÞD/ xi xi�1 . . . xi�ðm�1Þ� �� �

; 1� k�K

ð7Þ

where y[k] is the PSO-NN artificial feature of the IEEGsignal x(t), or the artificial feature time-series, i is theindex that controls the displacement inside the slidingwindow, and k is the discrete time unit of this timeseries (or the index that indicates the next sliding-window position). The sliding window observes all theL points in the window-position k whereas the index iis delimited by 1þ ðk� 1ÞD � i � Dðk� 1Þ þ L(superscript and subscript on the summation opera-tor). We selected i to advance by steps of one, thustaking advantage of all the IEEG data available andscrutinizing the data in maximum detail looking forpatterns.

Summarizing, (7) is the average of the PSO-NN-processed points observed through the sliding window,where the argument of the summation /ð�Þ is the

neural-artificial feature function optimized by the PSOalgorithm. At each window position k, a number ofpoints, L, will be reduced, by means of (7), to a singlepoint at the output. Thus, if we have an IEEG signalthat contains P points (samples) and we set displace-ment to D, the number of points in the artificial featuredomain is

K ¼ P� L

D

� �ð8Þ

Thus, the discrete time index of the artificial time-series k goes from 1 to K for each feature.

Classifier

The classifier component (see Fig. 2) selected were athreshold (further detailed later) and a k-nearest-neighbor rule,3 a non-parametric classifier able toperform non-linear boundaries and handle multi-modal classes. Additionally, the classifier is easy totrain; a process in which the labeled training data areprovided and stored in database-like scheme. Ideally,the classifier will perform as a detector that triggerswhenever a HFEO sample is presented.

Description of the Experiments

Data used for the experiments were explained inSection ‘‘IEEG Data Collection’’ Holdout validationwas used for the experiments, that is, a set of the datafor training and a set of the data for validation pur-poses. Four methodologies were evaluated and statis-tically tested and are explained as follow:

M1—the benchmark methodology for detecting epi-leptic precursors,18 which was based upon an

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FIGURE 4. An example of enhancement and feature extraction for an EEG time-series with epileptic high-frequency oscillationsfrom the pediatric subject, C07. (a) IEEG signal, where dots indicate epileptic high-frequency oscillations as marked by experthuman reviewers, while the question mark represents an event not marked by an expert that may or may not be an HFO, (b)whitened and high-pass filtered (enhanced) IEEG.

FIRPI et al.

unsupervised threshold of the values of a feature-series using root mean square (RMS). The selectedthreshold presumed that epileptic oscillations werestatistical outliers with values in a feature-series al-ways greater than an arbitrary number of standarddeviations (e.g., 2, 5) of the mean value of the RMSfeature-series.

M2—a variation of M1 that addressed key concernswith the way the threshold is selected. More spe-cifically, this method calculated the optimal point ofan ROC curve as the value of the threshold from thetraining data for the RMS feature. The supervisedtechnique of the latter modification was supportedby binary detection based on Neyman–Pearsontheory.3

M3—this method creates an optimal artificial featurevia PSO-NN and calculated the optimal point of anROC curve as the value of the threshold from thetraining data for the feature. Again, this modifica-tion was supported by binary detection based onNeyman–Pearson theory.

M4—this scheme that is discussed in Section ‘‘IEEGData Collection‘‘ using differentiation and band-pass filtering to enhance the time-series. A feature isderived via PSO-NN algorithm, and k-nearest-neighbor rule for classification, which the number ofneighbors was set to five and the Euclidean norm asthe distance metric.

The NN configuration for PSO-NN has six nodes inthe first hidden layer, eight nodes in the second one,and one node in the output layer, totaling 107 weights.The configuration was selected experimentally. Allnodes employed the logistic function as the activationfunction. As for the PSO algorithm, the particle has107 dimensions (i.e., the number of weights for theNN). The number of generations was set to 100 andthe number of particles was set to 50. The parametersc1 and c2 were set to 1.4962 (parameter values recom-mended in the literature), Xmax ¼ 10ðXmin ¼ �XmaxÞ,Vmax ¼ 5ðVmin ¼ �VmaxÞ; and a linear-decreasinginertia factor was implemented being initially set tow = 0.7298 till 0.4, making a linear transition fromexploration to exploitation mode, that is, from a globalsearch to a local one. The number of delays was set tom = 6. Thus, the input vector is x ¼ ½xi xi�1 xi�2 xi�3xi�4 xi�5�: The window length L was set to 20 points(or 0.1 s) and D to 12 points (or 0.06 s). The PSO-NNalgorithm was evaluated three times on the sametraining data to ensure robustness.

Finally, all four methods were run on the testingdata for each subject to evaluate the performance ofeach method in relation to the other. Standard metricsin pattern detection, the sensitivity and specificity, alsoreferred to as the probability of true detection and one

minus the probability of false alarm,3 were calculatedto estimate the performance of an approach. A two-factor analysis of variance (ANOVA) statisticallycompared the performances to determine if any sig-nificant differences were evident.

RESULTS

As mentioned previously, we executed the algorithmon training data from one pediatric patient, with awell-defined cortical dysplasia, and the other five IEEGdata sets from adult patients that were diagnosedas having non-lesional, extra-temporal epilepsy.Figure 4a shows an example segment of IEEG signalfor the pediatric case containing HFEO events andFig. 4b its preprocessed version. The two expert-marked HFEOs can be pinpointed in the enhancedsignal but also others HFEO-like events were revealedthat either might be HFEOs missed by the epileptol-ogist or false positives. Figure 5 presents the detectionfor the IEEG segment presented in Fig. 4 using a k-NN classifier along with the PSO-NN time-series,whereas Fig. 5b presents the HFEO detection bymeans of a threshold for the RMS feature. While thedetector using the k-NN classifier detected both eventsmarked by the epileptologists, the detector using atuned threshold missed an HFEO. Note that theamplitude of the PSO-NN feature decreased to nearlythe same level for both expert-marked HFEO eventsaffecting the classifier�s decision favorably. Such anoutcome was not true for the RMS feature given thatfor one HFEO the amplitude was large but for theother was not enough to trigger the detector.

Figure 6a presents an actual IEEG segment fromadult patient E03 with four HFEOs marked by theepileptologist and Fig. 6b the enhanced signal. Fromthe enhanced signal, the expert-marked HFEOs areclearly located but additional high-frequency-likeevents that either are HFEOs missed by the reader orare false positives. Figure 7a shows the detection forthe k-NN classifier along with the signal processedthrough the PSO-NN feature found whereas Fig. 7bshows the detection markings for the threshold detec-tor, after the filtered IEEG was evaluated through theRMS feature. For this IEEG epoch, the k-NN classi-fier detected all the expert-marked events while trig-gering a false positive. On the other hand, thresholdmissed two HFEOs, although the right-most HFEO-like did not trigger the detector. In both figures, eachvalue of the threshold was obtained by computing theoptimal point on an ROC curve of the subject-specifictraining data.

For all six subjects and four methods, a two-factorANOVA was evaluated to test the null hypothesis,

HFEO-Detection Using PSO-NN Artificial Features

which was that all approaches to detect an HFEO (i.e.,M1, M2, M3, and M4) performed equally interms ofsensitivity and specificity. The subject served as the firstfactor (5 degrees of freedom (DOF), no. patients ) 1)whereas the method (3 DOF, no. methods ) 1) as asecond factor for a total of 23 DOF. Table 1 shows theactual p-values for rejecting the null hypothesis that allmethods performed equally according to sensitivity(SENS) or specificity (SPEC). Moreover, since all

p-values were below the significance level of 5%, a Tu-key�s test was run to identify which method(s) differedfrom the others as depicted by the comparative box-plots in Fig. 8. In Fig. 8, the top plots juxtapose themethods by sensitivity and the bottom plots contrast themethods by specificity. As stated in the previous section,the M preceding a number denotes the different meth-ods evaluated, C07 refers to the pediatric patient, and Efollowed by a number refers to an adult patient.

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FIGURE 5. Feature extraction and classification results for a 4-s interval within a validation record from the pediatric subject (seeFig. 4) for the following methods: (a) PSO-NN feature and k-NN classifier (i.e., M4) and (b) RMS feature and ROC-tuned threshold(i.e., M2). In each plot (top and bottom), the solid curve is the enhanced EEG, the thick, solid curve is time-series for the feature, andthe dashed curve is the output of the detector. The output of the detector and the series for the feature are normalized to the scaleof the EEG.

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FIGURE 6. An example of enhancement and feature extraction for an EEG time-series with epileptic high-frequency oscillationsfrom an adult subject, E03. (a) IEEG signal, where dots indicate epileptic high-frequency oscillations as marked by expert humanreviewers, while the question mark represents an event not marked by an expert that may or may not be an HFO and (b) whitened,high-pass filtered (enhanced) IEEG.

FIRPI et al.

DISCUSSION

High-frequency epileptic oscillations (HFEOs) mayrepresent precursors of epileptic seizures and helplocalize the region of seizure generation, allowingneurologists to pinpoint accurately the epileptic net-work. HFEOs have been characterized as having a lowsignal-to-noise ratio and brief durations makingimperative an intelligent tool to detect them with highaccuracy. The key to such a tool, which parallels apattern classification problem, are the feature chosenand feature extraction module. Thus, an automaticfeature creator algorithm utilizing a PSO algorithmand NN was implemented to design artificial featuresthat explore and exploit particularities of the HFEOsand separate them from BS data in order to classify

them correctly using an empirical-tuned and ROC-tuned thresholds and k-NN as classifiers (detectors).The PSO-NN features were benchmarked with theRMS feature, which was implemented in a recent studyand taken as the standard in detecting high-frequencyoscillations.

Overall in this experiment, a classifier that processeda PSO-NN feature, especially for a k-NN classifier,surpassed the classification accuracy of a classifier thatprocessed the RMS feature. This result was evidentthrough a validation analysis in which the swarmedNN feature and the conventional feature shared thesame sets of training and testing data with the trainingand testing data differing in each set. In general, thetradeoff between the sensitivity and specificity waslower for the neural net-based features. This is a sig-nificant accomplishment because posterior analyses,such as statistically mapping and clustering HFEOactivity in the brain, require highly reliable and accu-rate information (i.e., correct HFEO events detected)as possible. That is, although the PSO-NN-baseddetector may exhibit a few more false positives than anRMS-based detector, the former detector will alsoachieve a much better sensitivity (trigger on more truepositives) than the latter scheme. Results demonstratedin two out of six cases in which no statistical differencebetween two or more methods in terms of sensitivity.For instance, for the pediatric patient C07, there is nostatistical significance among M2, M3, and M4. Apossible explanation was that the pediatric patientrepresented a daunting case—perhaps due to the

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FIGURE 7. Feature extraction and classification results for a 4-s interval within a validation record from the adult subject (seeFig. 6) for the following methods: (a) PSO-NN feature and k-NN classifier (i.e., M4) and (b) RMS feature with ROC-tuned threshold(i.e., M2). In each plot (top and bottom), the solid curve is the enhanced EEG, the thick, solid curve is time-series for the feature, andthe dashed curve is the output of the detector. The output of the detector and the series for the feature are normalized to the scaleof the EEG.

TABLE 1. Statistics for rejecting the null hypothesis.

Patient Source DOF F-value p-value > F

SENS C07 Subject 5 141731.65 0

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E07 Subj. · meth. 23 15607.83 0

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HFEO-Detection Using PSO-NN Artificial Features

dysplasia—in which the IEEG possessed poor contrastbetween background activity and the HFEOs activitydespite filtering. Nonetheless, this paper contributedthe importance of selecting a proper threshold derivedfrom the data (i.e., via training the threshold) whencomparing the results of M1 against M2-4 for thissubject. Continuing, the results for patient E02 werealso inconclusive, given that the difference in sensitiv-ity between M2 and M4 were statistically insignificant.However, again, this case also shows the importance ofselecting a proper threshold derived from the data.

One limitation of the PSO-NN routine in theseexperiments was that the configuration of the NN wasdesigned a priori. There might be smaller networks thancan extract the same level of pattern relevant, but alsothere might be cases where a larger network might beneeded. To address this, as future work, we will allowthe PSO algorithm to design the network structure suchthat the PSO-NN algorithm can construct a moreoptimal feature. On the other hand, in this work, thePSO-NN algorithm designed patient-specific detectors;

however, we are also interested in designing a universalartificial feature. More specifically, it would be advan-tageous to use the PSO-NN algorithm to design ageneric feature that can be used across patients avoid-ing the training process and having better detectionaccuracies than those provide by the patient-specificPSO-NN feature. To achieve this, data from a largersample of different patients will be needed to build thetraining dataset for the algorithm. However, con-structing a general feature—expected still to best theperformance of a classical feature such as RMS, sacri-fices the patient-specific ‘‘property.’’ That is, for thePSO-NN to design a general feature, it needs to exploreand explode the general characteristic present in thedifferent HFEOs coming from different patients, thus,it might not optimize the feature for the characteristicof slow-ripples coming from particular patient. There-fore, lower performance accuracies might be expectedbut better than those yielded by the RMS feature.

Another important aspect of the PSO-NN algo-rithm is the computational cost involved with it. The

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FIGURE 8. These box plots capture the statistically significant differences in terms of sensitivity (top panels) and specificity(bottom panels) among the four binary detectors (from left to right, M1 = RMS with threshold, M2 = RMS with ROC-tuned threshold,M3 = PSO-NN feature with ROC-tuned threshold, M4 = PSO-NN feature with k-NN rule) tested on data with ground-truth for sixpatients (left to right, C07, E02, E03, E05, E07, and E09). Excepting patients C07 and E02, M4 was proved the best detector. For C07,there was no statistical difference between the proposed method (M4) and the benchmark (M1 and M2) whereas for E02 there wasnot statistical difference between M2 and M4.

FIRPI et al.

PSO algorithm is an algorithm that involves the eval-uation of many individuals (i.e., NNs) and generations,a time-consuming process. However, one importantattribute of particle swarm algorithms is that they canbe implemented in parallel (just the evaluation of theindividual, though, and not the entire routine), cuttingdown the computational by number of processorsavailable. Equation (9) shows an approximation of thetime complexity order for the PSO-NN algorithm.

T ¼ s �N � GP

ð9Þ

where s is the time consumed to evaluate one indi-vidual, N is the number of individuals in a population,G is the number of generations, and P is the number ofprocessors in parallel. In our experiments, a stand-alone computer with a 3 GHz processor was used,taking one third of a second to evaluate one individual.Thus, evaluating (9) with parameters set in experimentsthe entire run took approximately T = 27 min per

patient. Obviously, the evaluation of the RMS featureis less time consuming but, fortunately, the evaluationof the PSO-NN algorithm is procedure performed off-line. Once the training, testing, and tuning steps areperformed, the final detector (i.e., PSO-tuned NN) canbe implemented in a prediction/stimulation device ormonitoring software.

Finally, as previously noted, results presented arefocused on the detection of HFEOs throughout chan-nels. However, once HFEOs are detected, a questionthat could be formulated is whether there is a rela-tionship among HFEOs detected in different channels(i.e., space) and the proximity of the onset of the sei-zure. Figure 9 depict 3-D plots for four patients: C07,E03, E05, and E07, which axes are time [min], chan-nels, and energy. Although, no complex pattern maybe noted by the naked-eye, in patients C07 and E03there is HFEO activity in some channels; in particular,there are some channels where energy is higher than inother channels when getting closer to seizure onset. On

FIGURE 9. This figure exemplifies the input to the module that statistically clusters the spatial-temporal distribution of EHFOs forfour subjects (a) C07, (b) E03, (c) E05, and (d) E07. It can be observed from the two seizure records that for subjects C07 and E03certain channels frequently experience interictal epileptic oscillations whereas for subjects E05 and E07 all channels experienceepileptic oscillation activity.

HFEO-Detection Using PSO-NN Artificial Features

the other hand, for patients E05 and E07, the HFEOsare spread out throughout all the channels (i.e., visu-ally, there is no preferred channel(s)) and energy in-crease and decrease in a periodic fashion for theprevious 2 h before the seizure�s onset (i.e., there is nolinear increment in energy with respect to the seizure�sonset closeness). To investigate if there is a pattern inthe HFEO activity through the channels that can helpus to predict the onset of a seizure, localize its focus inspace, and improve the study of ictogenesis, experi-ments are underway to use data mining to find patternsand relationship between the variables of interest.16

Such a solution is the ultimate extension of thismanuscript.

CONCLUSIONS

This study presents an algorithm to automaticallydetect the HFEOs; however, the algorithm can beimplemented and trained to detect any high-frequencyoscillation band on the HFO spectrum. We highlightthe fact that the outcome from the PSO-NN algorithmis well related to the HFOs that constitute the trainingdataset, which is marked by experts.

Furthermore, once the PSO-NN algorithm hasdesigned the optimal feature for a detector, it can beimplemented for real-time time detection either insoftware or embedded in hardware and installed inEEG recorder equipment for clinical use. Nonethe-less, the detection and categorization of the EHFOsare the first step of this research. As a higher level ofanalysis, a mining algorithm will be used to findspatiotemporal patterns across multiple channels thatassist the epileptologists to map epileptic networks.Such a contribution can aid localization of the epi-leptic area16 as well as improve the study of icto-genesis. For instance, considering an implantableelectrical stimulation device, the detection of EHFOscan assist an epileptologist in where to place theintracranial electrodes for better chances of seizurecontrol and relief. Naturally, this application raisesthe question on how much detection accuracy isneeded to achieve an accurate epileptic networkmapping, an avenue that certainly needs moreresearch in future work.

Finally, high-frequency oscillations can also lead torecognize the dynamics of the brain, such that a ten-dency for a normal state or pre-seizure state11 isobvious and in turn predict the state of the brain, asmentioned in Schiff et al.14 where the authors foundunique development dynamic properties for the start,development, and culmination of epileptic seizures.Clearly the value of such work is determining reliablebenchmarks for transitioning therapy and under-

standing of epileptic mechanisms from a retrospectiveto a prospective utility.

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