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Biomedical Signal Processing and Control 49 (2019) 404–418

Contents lists available at ScienceDirect

Biomedical Signal Processing and Control

jo ur nal homep age: www.elsev ier .com/ locate /bspc

EG-based seizure detection in patients with intellectual disability:hich EEG and clinical factors are important?

ei Wang a,∗, Xi Long a,b, Ronald M. Aarts a,b, Johannes P. van Dijk c,d,ohan B.A.M. Arends a,c

The Department of Electrical Engineering, Eindhoven University of Technology, The NetherlandsPhilips Research, HTC, 5656 AE Eindhoven, The NetherlandsThe Department of Clinical Physics, Epilepsy Center Kempenhaeghe, The NetherlandsUniversity of Ulm, Germany

r t i c l e i n f o

rticle history:eceived 14 July 2018eceived in revised form 6 November 2018ccepted 6 December 2018

eywords:EGeizure detectionntellectual disabilitymbalanced dataost-processingultilevel analysis

DAVM

a b s t r a c t

Epilepsy is a commonly secondary disability in people with an intellectual disability (ID), affecting 22% ofthe ID population while 1% of general population. Surprisingly, EEG-based automated seizure detectionin the ID population has not yet been sufficiently studied. The reasons are twofold. Firstly, long-termEEG recordings are few due to behavioral problems. Secondly, the annotation of EEG recordings has beenproved difficult due to the complex EEG signal abnormalities caused by brain development disorders. Asa result, the performance of automated seizure detection for ID people is largely unknown. In this work,we performed automated seizure detection on a retrospective dataset containing 615 h ambulatory scalpEEG from 29 participants with ID, including 91 seizures. To design a generic seizure detector for the IDpeople, we need to deal with three major problems: highly imbalanced data, heterogeneous dataset anddifficult annotation. (1) For the imbalanced data, we used proper performance criteria (e.g., precision andrecall curve) and employed a post-processing process (i.e., patient-specific detection thresholds). (2) Forthe heterogeneous dataset, we employed multi-domain EEG features that showed a better discrimina-tive power in our dataset, and compared the linear and nonlinear (LDA vs. SVM with Gaussian kernel)classifiers and validated using a leave-one-out cross validation (LOOCV). (3) A stepwise EEG annotationprocedure was used to improve the accuracy of annotation due to the presence of numerous seizureimitators and unclear contrast between ictal and interictal EEG activities. Results showed that LDA out-performed SVM with a clear margin of sensitivity, and achieved overall sensitivities 63.1–81.3%, a median

FD/h of 1.0 and median latency of 11.5 s. Finally, we conclude that EEG signals of the ID population forma heterogeneous entity with respect to important factors: EEG discharge patterns, EEG backgrounds andEEG seizure visibility. The performance of the seizure detection varies significantly with these factors.The results presented here can serve as prior knowledge for designing a generic seizure detector for theID patients and the non-convulsive seizure states (NCSS).

© 2018 Elsevier Ltd. All rights reserved.

Abbreviations: AL, seizure alarm length; AUCPR , area under curve (AUC) of P–Rurve; DT, detection threshold; EMG seizure, discharge with EMG activity; FDs,alse detections; FDt/h, time of FD per hour of recording; ID, intellectual disabil-ty; LOOCV, leave-one-out cross validation; LDA, linear discriminant analysis; NCSS,on-convulsive seizure states; PPV, positive predictive value; P–R, precision andecall; PS, prediction score; RUSBoost, random undersampling AdaBoosting; RF, ran-om forests; RBF, radial basis function; SP, fast spike seizures; SPWA, spike-waveeizures; SVM, support vector machines; WA, wave seizures.∗ Corresponding author.

E-mail addresses: Lei.Wang@tue.nl (L. Wang), xi.long@philips.com (X. Long),onald.m.aarts@philips.com (R.M. Aarts), DijkH@kempenhaeghe.nl (J.P. van Dijk).

ttps://doi.org/10.1016/j.bspc.2018.12.003746-8094/© 2018 Elsevier Ltd. All rights reserved.

1. Introduction

Abnormal brain development often results in an intellectual dis-ability (ID), which includes an abnormally low intelligence quotient(IQ). Epilepsy is a common secondary disability in people with ID[1], often beginning in childhood and affecting approximately 22%of people with ID compared with 1% of the general population [2–4].The seizures of ID people are often severe, frequent, and intractableto antiepileptic drugs [5]. Despite the strong association between

ID and epilepsy [6], literature on the specific EEG characteristicsof people with ID and epilepsy is scarce [7,8], and no systematicreports on the automated seizure detection in this population areavailable. An important reason may be that long-term video/EEG

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ecordings of ID patients are rarely performed due to behavioralroblems.

Current research of seizure detection in patients with an ID isimited, and varies from non-EEG detection of major convulsiveeizures [9–11] to EEG-based detection of minor seizures [12,13].he EEG signals of ID patients are difficult to interpret because ofbnormalities such as different ictal discharge types, backgroundbnormalities, highly variable proportions and forms of interic-al events, as well as unreliable annotations caused by a poorontrast between abnormal and normal EEG activities. Therefore,utomated analysis of EEG signals of ID patients is very challenging.urthermore, non-convulsive seizure states (NCSS) in ID patientsccur frequently and are underdiagnosed [14,15]. NCSS is a hetero-eneous entity [16], and the diagnosis cannot exclusively rely onEG changes [17]. Until now, to the best knowledge of the authors,o automated methods have been published for a reliable diagnosisf NCSS.

In our previous studies for the ID population, we evaluated thetate-of-the-art seizure detection methods on the basis of epilepticpochs [13] and proposed new EEG features [20] to improve theetection performance. In this more clinically-orientated work wery to answer the question: which factors (clinical and from theEG) are important for seizure detection in the ID population. Inontrast to our previous study, we evaluate the seizure detection inn event-based manner instead of epoch-based, and the detectionerformance for each individual patient is assessed. The seizureetection task is limited to the detection of EEG seizures in thisork. To avoid ambiguities, we specify the terminologies used in

his study as shown in Table 1. More revised EEG-related terms cane found in elsewhere [21]. This study also serves as a preparationor the automated detection of NCSS in the ID population.

The performance of automated seizure detection for ID patientss largely unknown. This work aimed (1) to construct an annotatedEG dataset of epileptic patients with an ID and (2) to develophe EEG-based seizure detector and evaluate the detection perfor-

ance in a systematic manner. The EEG-based seizure detector canelp neurologists to locate seizures in long-term EEG recordings foriagnosis, and contribute to a real-time monitoring system usinghe ambulatory equipment. The major contributions of this workre listed as follows.

We constructed the first, long-term EEG dataset of ID patientswith the hierarchical annotation (in .XML files) including bothEEG and non-EEG information.This work evaluated the real-life data (i.e. highly imbalanceddata) by using proper performance criteria, and we showed therelationship between the epoch detection performance (i.e., clas-sification performance) and event detection performance.This work optimized the seizure detection on the imbalanceddata by employing a post-processing process (i.e., patient-specificdetection thresholds), and we also evaluated the performanceusing predefined detection thresholds (DTs) (e.g., DT = 0.5).We employed multi-domain EEG features that showed a betterdiscriminative power in our dataset [13], and compared the linearand nonlinear classifiers (LDA vs. SVM) on this heterogeneousdataset by using LOOCV.Important EEG and non-EEG factors were recognized by using amultilevel analysis, which evaluates mixed effects of hierarchicalfactors.

This paper is organized into three major sections. Firstly, ‘mate-ials and methods’ motivated the study design, introduced the EEG

nd patient information, the seizure detection method and perfor-ance criteria, as well as the statistical analysis methods. Secondly,

results’ described the obtained EEG dataset and patient demogra-hy. We reported the detection performance in two ways: using

ng and Control 49 (2019) 404–418 405

predefined DTs and using patient-specific DTs. Important factorswere recognized and the relationship amongst them was furtherreviewed. Finally, we discussed the effects of the heterogeneousdata on classifiers. We compared our results with other studies onlong-term EEG and interpreted the clinical relevance of the findings(i.e., important EEG factors). A possible application for detection ofNCSS was also proposed.

2. Materials and methods

2.1. Study design

This is a non-randomized retrospective observational clinicaltrial. The aim of this study is to evaluate the EEG and clinical factorsthat potentially affect the detection performance. Given that thesefactors are not normally distributed in patients [9], a limited patientsample size could miss some rare factors, while a larger samplesize is more time-consuming for annotations. Therefore, we didnot perform a random patient selection from the ID population.Based on the seizure detection performance in our pilot study [22],we needed at least eight patients for each EEG discharge pattern( ̨ = 0.05, ̌ = 0.2).

2.2. Patient selection

We selected participants with an ID who showed at least oneEEG seizure in continuous 24-hour ambulatory EEG recordings witha good signal quantity. Clinical seizures without EEG change wereexcluded due to the lack of timing information between the clinicalevents and the EEG, since no synchronous video could be used inthis field study (at the patients’ home). We also selected a numberof patients who showed EEG seizures contaminated by EMG activ-ities (discharge with EMG activity). This seizure type accounts foraround 95% of clinical seizures in the ID population [9]. Note thatsuch a control of patient selection may induce a source bias, andmake the average detection performance less representative forthe whole ID population. However, the detection performance oneach seizure pattern thus is able to reveal. The study was approvedby Kempenhaeghe’s ethical review board.

2.3. EEG data

Patients with ID tend to suffer more severe behavioral problemsin a hospital environment, which makes it difficult to record a long-term video/EEG data. Therefore, all ambulatory EEG data of the IDpatients were recorded at home without video. The clinical infor-mation about the seizures was achieved from the diaries providedby caregivers, historical data and the final EEG reports. The con-tinuous scalp EEG signals (sampling rate of 100 Hz) were acquiredusing 24 electrodes (or channels) of Ag/AgCL in positions accordingto the 10–20 positioning system, measured by the EEG recordingequipment TMS (Twente Medical Systems) and reported with theEEG acquisition system BrainRT.

2.4. Annotations

A stepwise EEG annotation procedure was preferred instead ofa simple one-step approach with an inter-observer agreement test.At the first step, the EEG seizures were described by EEG technicianswhen preparing the EEG report. In the second step, all EEGs wereannotated more accurately by a clinical neurophysiologist special-ized in epilepsy. These annotations formed the basis of the final

selection of EEG data and included the onset and offset of EEGseizures, type of onset/offset (clear/blurry) and the types of ictalEEG discharge patterns. The four EEG discharge patterns defined inthis study (fast spike, spike-wave, slow wave and EMG) may occur

406 L. Wang et al. / Biomedical Signal Processing and Control 49 (2019) 404–418

Table 1Terminology used in this study.

Terminology Definition in this study

(Clinical) Seizure A transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain. In clinicalpractice, a clinical seizure is often diagnosed based on the clinical signs supported by the EEG manifestations [18].

EEG seizure EEG discharge that is associated with a clinical seizure. Note that clinical seizures may not show clear EEG change because two-thirdsof the cortex is enfolded in sulci and dipole discharges in sulci do not always project to scalp EEG electrodes [19].

(EEG) seizure onset The beginning time of a EEG seizure, i.e., the time that EEG signals begin to show any change in relation to a clinical seizure. Note thatthe EEG onset of a seizure could be earlier or later (from seconds to minutes) than the clinical seizure onset.

(EEG) discharge pattern The epileptiform discharges with certain EEG morphologies. Typical EEG discharge patterns include (fast) spikes, spike-wavecomplexes, rhythmic (slow) delta/theta waves. These were termed as ‘EEG seizure patterns’ in our previous study [13]. These EEGdischarge patterns are associated with specific clinical seizure types.

Discharge with EMG activity EEG seizures accompanied by electromyography (EMG) activity. They occur often in motor seizures such as tonic and myoclonicseizures in the ID population.

(EEG) seizure visibility Whether boundaries (onset and offset) of an EEG seizure based on visual inspection are clear or blurry. EEG seizures with clear (orblurry) boundaries are termed as (in)distinct seizures. Indistinct seizures can be located by using the clinical information and longercontext EEG activities.

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ig. 1. A comparison between a distinct EEG seizure (subj #26) and an indistinct onnd blue (dash) lines show the offset. For easy visualization, the EEG signals were p

n sequences or in all possible combinations (polyspike complexes)uring an EEG seizure. Combined patterns at the same time werelassified as mixed patterns. For indistinct seizures (see Fig. 1), wesed the context EEG activities to determine the onset and offset.

n the third step, doubtful EEG epochs were scored independentlyor a second time by two authors. If no agreement was present withhe first scoring, the EEG epochs were excluded for further analysis.he excluded EEG epochs in total were less than 5% of all seizure

EG epochs that accounts for only 0.14% of whole EEG recording.herefore, it has little effect on the reported performance.

j #3) with fast spike discharge pattern. Red lines show the onset of an EEG seizure by using a common average montage.

We also recorded patients’ wake and sleep status that wouldpotentially cause differences in EEG signals [23]. Due to the lackof automated classification methods of sleep/wake status in thisID population, the wake and sleep status was estimated from thediaries and the classification of the sleep differentiation by theEEG technicians. Note that one patient could have more than oneawake stages during sleep. The boundary between awake and sleepstates could have several minutes bias, therefore, the EEG seg-

ments (<1%) around the boundaries are excluded for statisticalanalysis.

L. Wang et al. / Biomedical Signal Processi

Table 2Patient demographics including EEG and clinical factors.

Factors Median Range

Age (yrs) 28 12–51Seizure number 2 1–13Average seizure duration (s) 19 5.5–159.5EEG recording length (h) 22.5 17.4–25.8Gender 17 males and 12 femalesID level* 3 light, 11 moderate, 15 severeClinical seizures** 21 tc, 16 ton, 9 myoc, 11 unclassifiedSeizure patterns*** SP = 12, SPWA = 6, WA = 9, EMG = 9, Mix = 6EEG backgrounda Normal (n = 14), 1 (n = 8), 2 (n = 5), 3 (n = 2)Interictal spike levelsb 1 (n = 6), 2 (n = 4), 3 (n = 10), 4 (n = 9)Occipital EEG typesc Normal (n = 9), 1 (n = 6), 2 (n = 10), 3 (n = 4)Seizure visibilityd Yes (n = 13), no (n = 16)Sleep typese Normal (n = 4), 1 (n = 13), 2 (n = 6), 3 (n = 6)

* IQ levels are 3 (severe, IQ < 30), 2 (moderate, 30–50), and 1 (light, 50–70).** It records the number of subjects who have specific clinical seizure events, tc -

tonic–clonic, ton – tonic, myoc – myoclonic, others – unclassified types [18].*** The four discharge patterns fast spike, spike-wave, wave, discharge with EMG

activity and Mixed patterns are shortened as SP, SPWA, WA, EMG and Mixed, respec-tively.

a EEG background types are normal (i.e., symmetric), 1 (regional abnormality), 2(hemispheric abnormality), and 3 (bilateral abnormality).

b Interictal spike levels are normal (none), 1 (<1%), 2 (1–10%), 3 (10–50%) and 4(>50%).

c Occipital types are normal (8–10 Hz), 1 (6–8 Hz), 2 (4–6 Hz) and 3 (2–4 Hz).d

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Whether the boundaries of EEG seizures are clear or not? [19].e Sleep types are 1 (no phasic events such as K-complex, sigma spindles, normal

leep stages), 2 (only NREM/REM), and 3 (unclassified).

.5. EEG factors and clinical factors

Clinicians rely heavily on EEG discharge patterns to identifynd localize clinical seizures [24]. The performance of automatedeizure detection varies with different EEG discharge patterns13,25]. We used the following typical EEG discharge patterns26,27] associated with clinical seizure types defined by the Inter-ational League Against Epilepsy (ILAE) [18]:

(fast) spikes,spike-wave complexes (or sharp/slow waves),rhythmic (slow) delta/theta waves.

Fast spikes often present during tonic seizures. Spike-wave pat-erns occur during myoclonic seizures, or at the end of tonic–cloniceizures. Rhythmic slow delta/theta waves may present duringocal seizures. These three typical EEG discharge patterns arelso generally described as polymorphic seizure patterns [18]. Weefined an additional discharge pattern: discharge with EMG activ-

ty, which often exists in most motor seizures including tonic,onic–clonic and myoclonic seizures. A previous study [9] in theD population suggests that 95% of the clinical seizures are motoreizures, often accompanied by EMG activities. Therefore, it is notroper to simply exclude the seizure types as EMG artifacts [28].

In addition, the influence of EEG seizure visibility (defined aslear or blurry boundaries of an EEG seizure when doing visualnnotation) [19,29]. The ID patient’s EEG background (continuouslyngoing activities [21]) is often abnormal due to pathological con-itions of the brain [30]. An abnormal EEG background is associatedith intractability. Clinical factors including patient’s age [31], IQ

evel and sleep differentiation [23] were took into account. Seeable 2 for detailed EEG and non-EEG factors.

.6. Detection methods

Our seizure segment detector is to identify a segment of the EEGischarges (including ictal and interictal) that may be accompaniedy an EEG seizure. The detector is composed of four major units,

ng and Control 49 (2019) 404–418 407

namely, EEG preprocessing, feature extraction and normalization,classification and post-processing. An illustration of the detector isshown in Fig. 2. For a more detailed description of the EEG featuresspecialized for this ID population, we refer to our preliminary work[13]. The final determination of an EEG seizure depends on the def-inition of a seizure event, which however is different cross studies[32].

2.6.1. EEG preprocessingIn this work, we set a preprocessing rule that allows not only

EEG signals but also a certain amount of artifacts (e.g., EMG) to bekept for the EEG seizure detection. The unipolar montage is used toavoid changing the synchrony among EEG channels [33]. Three EEGelectrodes above eyes, Fp1, Fpz, and Fp2 are excluded because thesignals are contaminated by the successive eye blink/movementartifacts. Firstly, on each EEG channel, the signals are filtered byusing a 10th-order Butterworth bandpass filter with the lower andthe higher cutoff frequency of 0.5 Hz and 45 Hz, respectively. Sec-ondly, EEG channel selection has been performed to choose thechannels that contain EEG with good signal quality by using athreshold policy. That is, in each non-overlapping sliding windowsof two seconds, we keep only the channel in which the amplituderange ra of the EEG epoch is within [10–200] �V for further analy-sis (ra = (max(x) − min(x))/2, where x is the amplitude sequenceof an EEG segment). The lower boundary (10 �V) was to rejectartifacts caused by loose electrode-skin collection or sweating. Thehigher boundary (200 �V) was to reject excessive artifacts causedby movements, electrocardiogram (ECG), and excessive EMG activ-ities.

2.6.2. Feature extraction and normalizationOur previous study [13] proposed a large multi-domain fea-

ture set (including the traditional and newly-proposed features)and reported a significantly improved classification performancecompared with a conventional feature set. Therefore, the EEG fea-ture set proposed in our previous study was used here. It includes47 features in the time, frequency, time-frequency, and spatio-temporal domains, as well as synchronization-based features. Tospeed up the training process of the classifiers (e.g. SVM), we usednormalization of standardized ‘z-score’ (i.e., z = (x−�)

� ) to linearlymap each feature into a common scale with an average of zero andstandard deviation of one. Note that for each feature, the mean �and standard deviation � are estimated on the entire feature space(including all patients’ data) instead of on only each subject. Sincethe same linear transformation was performed for all subjects, itdid not affect the separability of seizure and non-seizure classes forcross-subject classification validation. Otherwise, the distributionof each feature can be changed in the feature space, if the z-scoreis performed on each subject’s data separately.

2.6.3. Classification and post-processingSeveral classifiers with different complexities were used for

validation of classification performance. They are the linear dis-criminant analysis (LDA), support vector machines (SVM) withGaussian kernel, random forests (RF) and random undersamplingadaptive boosting (RUSBoost). More descriptions about these clas-sifiers can be found in [13].

In addition to the EEG features and classifiers, the post-processing of classification also plays a role [34,35]. For example,a Kalman filter [36] and firing power [37] were used to reduce thenoise of outputs of SVM classifiers. A sequential prediction score(PS) (or seizure probability [35]) between 0 and 1 is obtained by a

linear mapping from classifiers’ output and further compared witha decision threshold (DT) for a decision making [38]. On imbal-anced data, classifiers with default thresholding (i.e., DT = 0.5) is notoptimal. However, classifiers with a proper threshold can outper-

408 L. Wang et al. / Biomedical Signal Processing and Control 49 (2019) 404–418

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ig. 2. An illustration of the EEG-based seizure segment detector. A 2-s segment weature extraction, we obtain a feature vector including 47 features and perform applied and the final output is a number 0 (non-seizure segment) or 1 (candidate se

orm other methods specialized for imbalanced classification suchs resampling and/or instance weighting [39]. Therefore, insteadf using default DTs, we propose to use thresholding (i.e., DT varyetween 0 and 1) to find optimal patient-specific DTs. In addition,e evaluated detection performance by using predefined DTs with

ifferent values.

.6.4. Definition of the detection of seizure eventThe reported performance depends on more or less the defi-

ition of a seizure detection. The method of ‘any overlap’ being aetection tends to report an over-optimistic performance [40], andhe approach that sensitivity and FD are separately evaluated bysing different rules is also not realistic in a clinical practice. There-

ore, we did not apply a rule such as that FDs occurring within a timepan (30 s) are counted as only one FD [41].

The event-based seizure detection in this work (see Fig. 3) is per-ormed three steps. First, the output of a classifier is mapped intohe range between 0 and 1 (normalized PS) in the post-processing.he normalized PS is then converted into a binary sequence of 0non-seizure) and 1 (candidate seizure) by using a DT. Second, tolter the isolate seizure-like events during interictal EEG, we define

shortest alarm length (AL). That is, only continuous seizure can-idate epochs that last longer than AL can trigger a seizure alarm.inally, we compare raised seizure alarms with the experts’ annota-ion. The seizure alarm overlapping with experts’ annotation countss a detected seizure. Otherwise, it is an FD. Choosing a longer ALan reduce FD, but at a cost of a longer latency and missed detec-ion of short seizures. Three 2 s epochs [25], past 25 s epochs as aaseline [42] or a sliding window of 20 min [43] are used to make

seizure decision. In our dataset, the majority of seizures werehort. We report the detection performance with AL of 2 s, 6 s, and0 s (i.e., the length of a sliding window), respectively.

.7. Evaluation of detection performance

.7.1. Leave-one-out cross validation (LOOCV)

In a LOOCV scheme, a classifier was trained on the pooled fea-

ure set from all but one of the patients’ EEG recordings, and it wasubsequently used to classify/predict the seizure and non-seizurepochs in the excluded patient. This was repeated until each patient

lti-channel raw EEG signals is fed in the detector. After the EEG preprocessing andre normalization. To further optimize the output of classifiers, post-processing is

segment).

was excluded and tested once. For each patient, we report a detec-tion performance.

2.7.2. Criteria for classification performanceThe precision and recall (P–R) curve, i.e., a plot of the sen-

sitivity vs. positive predictive value (PPV), is known as a moresuitable performance metric than the receiver operating charac-teristic (ROC) curve (i.e., a plot of the sensitivity vs. 1-specificity)in imbalanced datasets [44]. Our previous study demonstrated thatthe area under the curve of a P–R curve (AUCPR) is a more discrimi-native indicator of classification performance in the skewed dataset(i.e., seizure epochs account for only 0.14% of entire EEG record-ings in our dataset) [13]. A larger value of AUCPR corresponds to abetter epoch-based detection performance. We further show theassociation between AUCPR and the event-based performance.

2.7.3. Determine a patient-specific detection threshold (DT)The P–R curve can show the overall classification performance

through AUCPR, and it can also determine a patient-specific DT. Foreach patient, we can determine an optimal DT by finding a thresh-old (between 0 and 1) that maximizes the F1 score (or F-measure)[39] on the P–R curve (see Fig. 4). When we increase a DT, the PPVincreases (the FD drops), but at the cost of a decreasing sensitivity.Note that a patient-specific DT is posterior (for off-line analysis)since it requires the global EEG data of a patient. The approach ofadaptively choosing an optimal DT in a real-time detection is stillan open issue and under development.

2.7.4. Criteria for event-based detection performanceThe event-based sensitivity is defined as the ratio between the

number of detected seizure events to the number of all annotatedseizure events. The latency is the time lag between the annotatedonset and the location of a seizure alarm. To make the perfor-mance comparable among datasets with different sizes (especiallythe recording length of interictal EEG), the FD rate is defined as theaverage number of FDs per hour (FD/h = #FD/the hours of entire

recordings). In addition, to reports the accurate agreement betweena classifier’s prediction and expert’s annotations [40], accumulatedtime (s) of FD per hour (FDt/h) proposed in our previous study [13]was used.

L. Wang et al. / Biomedical Signal Processing and Control 49 (2019) 404–418 409

Fig. 3. The definition of event-based seizure detection from the prediction score (PS) of a classifier. The ‘S’ and ‘NS’ denote seizure and non-seizure, respectively. The alarmlength (AL) is a predefined shortest time to trigger a seizure alarm, i.e., if the continuous ‘S’ in the binary score is longer than AL, a seizure alarm is triggered. Comparing withexperts’ annotation, an FD or a detected event with certain latency can be reported.

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ig. 4. An example of determining a patient-specific DT based on the P–R curveSubj#16, AUCPR = 0.44). The circle shows the point with maximum F1 score, and itorresponds to a DT with value 0.9819 on a normalized PS between 0 and 1.

.8. Statistical analysis

In our dataset, each patient has score (continuous or categor-cal) variables (e.g., seizure duration and IQ levels) and groupingariables (e.g., seizure patterns), which makes a hierarchical datatructure where variables (EEG/clinical factors) are nested. Toetermine the important factors that affect seizure detection, weerform a multilevel analysis by using a generalized linear mixed-ffects (GLME) model [45], which takes structural variables withxed and random effects measured at multiple hierarchical lev-ls into account [46]. Fisher’s exact test was used to test randomor non-random) associations between two categorical factors (at% significance level). Mann–Whitney test was used to compareetection performance between two patient groups. A two-sideruskal–Wallis test ( ̨ < 0.05) was used to compare detection per-

ormance among multi-groups. The Chi-Square test was used foresting the homogeneity of two subgroups of patients with multipleEG/clinical factors, e.g., discharge patterns, and IQ levels.

. Results

.1. Patients and demography

We finally included 29 epileptic patients (12 females, age9 ± 13 y) with an intellectual disability (3 light, 11 moderate, 15evere, with IQ at range of severe [0–30], moderate [30–50] andight [50–70]) for our EEG analysis. The selection procedure is

Fig. 5. Flowchart of the patient selection procedure.

shown in Fig. 5. The EEG dataset of the selected 29 epileptic patientshas a total EEG recording time of 615 h and contains 91 seizures (89generalized, 2 focal), including 21 patients showing tonic–clonicseizures, 16 tonic seizures, 9 myoclonic seizures and 11 unclassifi-able seizure types. The accumulated duration of EEG seizures across29 patients is 3034 s.

We used categorical score or grouping to record each patient’sEEG/clinical factors, as shown in Table 2. Patients’ usual clinicalseizure types defined by ILAE were obtained from their medicalhistory. Note that not all historical seizure types are present in thisdataset. Therefore, the historical seizure types were not used forfurther analysis.

3.2. Event-based detection performance

To understand the influence of a decision threshold (DT), wefirst report the event-based performance of seizure detection byusing predefined DTs. We then report the performance by usingpatient-specific DTs (i.e., post-processing), which shows an opti-mized trade-off between the sensitivity and FD rate in each patient.

3.2.1. Performance of predefined decision thresholds (DTs)We tested predefined DTs with values ranging from 0 to 1 with

a step size of 0.1 on both classifiers LDA and SVM. A DT with a fixedvalue is used for all patients to perform the seizure detection. Fig. 6

410 L. Wang et al. / Biomedical Signal Processing and Control 49 (2019) 404–418

Fig. 6. Detection performance on interval values of threshold (AL = 6 s). The number of detected seizure is equal to N * sensitivity, where N (N = 91) is total number of thes ur (the mean value and standard deviation on 29 patients). To report a meaningful result,t ate measure than commonly-used FD/h (counting only FD number) because FD/h wouldr hich is meaningless.

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Fig. 7. Number of detected seizures with respect to seizure duration (AL = 6 s).

Fig. 8. Average FD/h during wake and sleep status across all 29 patients (AL = 6 s).

eizure events in the dataset. FDt/h is the accumulated time (s) of FD epochs per hohe value of threshold 0.0001 was used instead of 0. Note that FDt/h is a more accurecord only one (or very few) FD that lasts hours when the threshold is close to 0, w

hows that, with the increasing of the threshold, both classifiers’Dt/h reduces, but the sensitivity decreases simultaneously. LDAeems less sensitive to the threshold compared with SVM sinceDA produces a more stable performance in a large range of DTsbetween 0.1 and 0.9) than SVM, which also shows a longer FDt/hhat may be undesirable in clinical practice. Two examples (seeigs. 15 and 16 in Appendix A) show the detailed output of LDA andVM on same patients.

.2.2. Performance of patient-specific decision thresholds (DTs)We use LDA to perform the seizure detection and report the

vent-based performance using patient-specific DTs. The LDA alsochieved better performance than other classifiers when classifyingon-seizure and seizure EEG epochs in most patients (see Fig. 13 inppendix A).

The AL should be smaller than a seizure duration to allow aight seizure alarm. We report detection performance of both LDATable 3) and SVM (RBF kernel) (Table 4) using AL with values of

s, 6 s and 10 s. A longer AL led to less FDs, but a reduced sensitivitynd a longer latency. The large number of FD might be caused byumerous seizure imitators during interictal EEG in this ID pop-lation. The LDA achieved a better performance (clear margin ofensitivity) than SVM: sensitivity 75.0% with median FD/h of 1 andatency of 11.5 s when AL is 6 s. The detailed detection performancef LDA on each patient is shown in Fig. 14 of Appendix A.

The accumulated number of detected seizures with respect toeizure duration is shown in Fig. 7. It shows that the seizures with

longer duration are easier to detect than the shorter ones. Theeizures longer than 60 s were 100% detected. In total, 23 out of3 seizures during wake and 27 out of 48 seizures during sleepere detected. There is no significant association found between

he sensitivity and the status of wake or sleep (p = 0.6837, Fisher’sxact test). However, there were significantly less FDs during sleephan wake status (Fig. 8, paired t-test, p = 0.0162). Most undetectedeizures are short. The possible factors are further addressed in thetatistical results.

.3. Statistical results of detection performance

We first demonstrate the association between AUCPR, an indi-

ator of classification performance (non-seizure vs seizure EEGpochs) and realistic, event-based detection performance (e.g. sen-itivity and FD/h). We then statistically recognize the importantEG and clinical factors that affect detection performance.

The FDs are counted on the recognized awake/sleep EEG segments. FDs during sleepare significantly less than during wake (paired t-test, p = 0.0162).

L. Wang et al. / Biomedical Signal Processing and Control 49 (2019) 404–418 411

Table 3Detection performance of LDA by using different alarm length (AL).

AL(s) No. of detectedseizures/seizureslonger than AL

Sens. a (%) FD/h (median, range) FDt/hb (median, range) Latency(s) (median, range)

2 74/91 81.3 2.87 12.3 7.5[0.08, 131.0] [0.1, 1455] [4.0, 60.5]

6 57/76 75.0 1.00 10.3 11.5[0.04, 75.2] [0.2, 1447] [8.0, 64.5]

10 41/65 63.1 0.17 3.2 16.0[0, 42.4] [0, 1426] [12.0, 68.5]

a Seizures shorter than AL cannot trigger a seizure alarm, thus are excluded when computing sensitivity.b Accumulated time (s) (per hour) of all FD epochs.

Table 4Detection performance of SVM by using different alarm length (AL).

AL(s) No. of detectedseizures/seizureslonger than AL

Sens. * (%) FD/h (median, range) FDt/h** (median, range) Latency(s) (median, range)

2 63/91 69.2 3.50 12.5 6.7[0.12, 97.0] [0.2, 1819] [4.0, 62.0]

6 41/76 54.0 0.58 5.6 10.0[0.04, 52.8] [0.2, 1818] [8.0, 66.0]

10 25/65 38.5 0.12 1.5 14.0[0, 38.8] [0, 1817] [12.0, 38.0]

* Seizures shorter than AL cannot trigger a seizure alarm, thus are excluded when computing sensitivity.** Accumulated time (s) (per hour) of all FD epochs.

Fig. 9. Detection performance of each patient (each point) by using the patient-specific DTs when AL = 6 s. The color represents the value of AUCPR. The capital lettersrepresent a patient’s major discharge pattern: A (SP), B (SPWA), C (WA) and D (EMG).The dashed line is to guide eyes: 13 out of 14 patients at the left show AUCPR > 0.1,wm

3d

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Table 5p values of GLME model variables.

Variablea p value Significancec

Visibility 2.9904e−08 ***Pattern: WA + EMG 0.0008 ***Background 0.0017 **Pattern: SPWA 0.0229 *Pattern: SP + SPWA 0.0292 *Pattern: SP + WA 0.0717 nsInterictal 0.0997 nsPattern: EMG 0.1908 nsPattern: SP 0.2333 nsDurationb 0.2891 nsOccipital type 0.4936 nsIQ 0.5577 nsAge 0.9731 ns

a Variables are ranked according to p values.

hile 15 patients (AUCPR < 0.1) are at the right. Note that six patients who show twoajor patterns are denoted by two capital letters, e.g., ‘AB’.

.3.1. AUCPR: overall classification performance for imbalancedata

We used the patient-specific DT, i.e., an optimal trade-offetween sensitivity and PPV (1-PPV = FD rate), with an AL of 6 so perform the event-based seizure detection on each patient.lthough the AUCPR can show the overall classification perfor-ance, it is sensitive to the imbalanced ratio, and the more

mbalanced the data is, the lower the AUCPR [39]. To compare acrosstudies with different imbalanced data levels, we also computedhe sensitivity and FD/h, see Fig. 9. It shows the detection perfor-

ance by using the patient-specific DTs. A larger AUCPR represents better detection performance with either a higher sensitivity, a

ower FD/h, or both. Given the highly imbalanced ratio of our EEGataset (i.e., seizure epochs accounts for only 0.14% of whole EEGecordings), we found that the AUCPR larger than 0.1 showed a rel-

b Duration is the accumulated time (s) of all EEG seizures on each patient.c *** p < 0.001; ** 0.001 < p < 0.01; * 0.01 < p < 0.05; ns: p > 0.05.

atively good detection performance, i.e., sensitivity > 0.5, FD/h < 1.The AUCPR lower than 0.1 corresponds to either a low sensitivity ora high FD/h.

3.3.2. Multilevel modelingThe GLME models the relationship between the detection per-

formance (AUCPR) and independent variables, i.e., EEG/clinicalfactors. Table 5 shows the important variables (with non-zero coef-ficients) and p values of their non-zero coefficients. Among allthe EEG/clinical factors. Three factors including the visibility ofEEG seizure boundaries, the EEG discharge patterns and EEG back-ground are found to be significant with a confidence level of 95%.The four discharge patterns fast spike, spike-wave, wave and dis-charge with EMG activity are shortened as SP, SPWA, WA and EMG,respectively. Mixed patterns are denoted by containing patterns(e.g., WA + EMG).

3.3.3. Important EEG factorsSeizure visibility: The seizure visibility was found to be the most

important factor in the GLME model. Fig. 10 shows the detection

412 L. Wang et al. / Biomedical Signal Processing and Control 49 (2019) 404–418

Table 6Across-patient seizure detection on long-term EEG data.

Studies Performance EEG dataset Methods

Saab et al. [42] Sensi. 76.0%, FD/h of 0.34, median DD of 10 s 28 patients, 652 h of EEG, 126seizures

Bayesian method, wavelettransform

Furbass et al. [41] Sensi. 81%, FD/h of 0.29 on 2-center data; sensi.of 67%, FD/h of 0.32 on CHB-MIT

3-center data: 205 patients, 310retrospective patients, andCHB-MITa

A computational method namedEpiScan

Direito et al. [50] Sensi. 38.47% and FD/h of 0.2 European epilepsy database, 216patients, 16,729 h

multiclass SVM (linear kernel),spectral analysis

Mathieson et al. [38] Sensi. of 52.6–75.0%, 0.04–0.36 FD/h 70 babies from 2 centers SVM, features in thetime-frequency domain

This work LDA: Sensi. of 63.1–81.3%, median FD/h of 1.0,latency of 11.5 s; SVM: Sensi. of 38.5–69.2%,median FD/h of 0.58, latency of 10.0 s

29 ID patients, 615 h EEG with 91seizures

LDA and SVM (RBF kernel),multi-domain features

Our previous studyb Sensi. of 68%, PPV of 81%, FDt/h of 0.76 s 29 ID patients, 615 h EEG with 91seizures

SVM (RBF kernel)

a CHB-MIT: the Children’s Hospital of Boston-Massachusetts Institute of Technology dataset [51], which is the biggest freely available dataset by far.b It performed a seizure pattern-specific detection on the same dataset and found that SVM achieved the best performance amongst several classifiers.

Fig. 10. Boxplot of AUC of patient groups with distinct (n = 13) and indistinct(

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Fig. 11. Boxplot of AUCPR of patient groups with the EEG discharge patterns: SP(n = 12), SPWA (n = 5), WA (n = 9) and EMG (n = 9). Note that six patients show twomajor discharge patterns and were counted twice.

performance in patients with a normal background is significantly

PR

n = 16) seizure boundaries (Mann–Whitney test, p = 0.00001).

erformance of patients (n = 13) with distinct seizures is signifi-antly different from those (n = 16) with indistinct seizures.

However, a Chi-square test shows that the patients in thewo groups are not comparable with respect to the factors: EEGischarge patterns, IQ level and EEG background (p = 0.0121).herefore, we performed a Fisher’s exact test between visibilitynd other factors. The EEG background was found to show a non-andom association (p = 0.0073) with visibility with an odds ratiof 10. This means that patients with abnormal background havebout 10 times greater odds of showing indistinct EEG seizurehan patients with a normal background. Interestingly, eight out ofine patients with discharges with EMG activity show distinct EEGeizures. Therefore, visibility of EEG seizures may be another possi-le reason why discharges with EMG activity has a better detectionerformance.

Discharge patterns: We compare the detection performanceUCPR of four patient groups according to the discharge patterns

Fig. 11). The Kruskal–Wallis test shows that the distribution ofUCPR is different across four groups (p = 0.02). Patients with dis-harge pattern of EMG show the highest AUCPR. Patients withhe discharge patterns of SP and WA show very low median val-es due to the detection failure on many patients (AUCPR < 0.1).y viewing the EEG signals of those patients, we found com-on EEG characteristics: (1) indistinct seizure boundaries; (2)

xtremely short seizures (i.e., T < 6 s); (3) focal or regional EEG

hanges during seizures (often associated with wave seizures);4) poor contrast between seizure and seizure-like interictal EEGithin/across patients.

Fig. 12. Average AUCPR of patient groups with EEG background types: normal (sym-metric, n = 14); 1 (regional abnormality, n = 8); 2 (hemispheric abnormality, n = 5); 3(bilateral abnormality, n = 2).

EEG background: The seizure detection performance declineswith the increasing abnormality level of EEG background (seeFig. 12). In addition, we compared the AUCPR of two groups:the patients with normal EEG backgrounds and the patients withabnormal EEG backgrounds (including scores 1, 2, 3). The detection

better than those with an abnormal one (Mann–Whitney test,p = 0.001). The Chi-square test shows that the patients in the twogroups (normal and abnormal background) are homogeneous (sig-

L. Wang et al. / Biomedical Signal Processing and Control 49 (2019) 404–418 413

Fig. 13. Comparison of classification performance on each patient in LOOCV by using linear discriminant analysis (LDA), random forests (RF), random undersamplingAdaBoosting (AdaBoost) and SVM with Gaussian kernels, respectively. The numbers of patients with AUCPR larger than 0.1 are 13 (LDA), 9 (RF), 9 (AdaBoost) and 8 (SVM).

Fig. 14. Event-based detection performance with AL = 6 s. The sensitivity is the proportion of detected seizures in the total number of seizures in each patient. The log scale isused to show FD/h. The latency is an average value on all detected seizures in one patient. All latencies are larger than 6 s. The null value of latency of some patients indicatesthat seizures are not detected in the patient.

Fig. 15. Normalized output (PS) of classifier LDA and SVM on the same patient (Subj#8, around 24 h EEG). Ann is experts’ annotation showing the locations of 12 seizures (0:non-seizure, 1: seizure). The LDA (AUCPR = 0.40) achieves a better classification performance than SVM (AUCPR = 0.24) on this patient. The optimal DTs are: DT = 1.2867e−04(LDA), and DT = 0.5285 (SVM).

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14 L. Wang et al. / Biomedical Signal Pr

ificance level of 0.05) with respect to the factors: EEG dischargeattern, IQ level and seizure visibility. Typical examples of nor-al and abnormal EEG background are shown in Figs. 17 and 18

n Appendix A.

. Discussion

Results show that the detection performance varies substan-ially across individuals. The variance of results is mainly causedy the different EEG factors across patients including the four dis-harge patterns, EEG background and seizure visibility. There wereo clinical factors associated with the detection performance of EEGeizures. These findings are important for future applications suchs the detection of NCSS [15].

.1. LDA versus SVM

A complicated classifier (e.g., SVM with Gaussian kernel) didot perform better than a linear classifier (i.e., LDA) in thecross-patient seizure detection. This is mainly caused by the het-rogeneous EEG data of the ID population. The performance of alassifier on a new patient mainly depends on the deviation of theew patient from a learned distribution (on the training dataset)

n the feature space. LDA tends to achieve better performance thanVM when the deviation of new data is large, while SVM tendso perform better when the deviation is smaller. Note that theeviation is small or almost close to zero in the patient-specifici.e., within patients) detection and the seizure pattern-specificetection (if the EEG data is randomly divided into training andesting sets [13]). Indeed, it was found that in large-scale het-rogeneous data, a linear classifier is often more robust than aonlinear classifier [47]. The classifier ensembles may be anotherolution for the heterogeneous data problem [48]. In the case ofur dataset, an ‘averaged’ performance between LDA and SVM cane more desirable because it may achieve generally higher clas-ification performance on more patients. The patient-specific DTan be viewed as a post-processing of classification, which aims toelp minimize the negative effects caused by the unbalanced andeterogeneous data problem. In practice, if longer EEG recordingsseveral days) of a specific patient are available, one day’s data cane used to obtain a patient-specific DT. In this case, the patient-pecific DT can help avoid a huge failure (e.g., too many FDs) on anxtremely ‘abnormal’ patient. In addition, adjusting the length ofLs may help further improve performance, especially for an on-

ine seizure detection. The choosing of an appropriate AL shouldepend on the prior information of an individual subject. Anotherndergoing project that our group is involved is performing a morextensive data collection (hundreds of patients) to obtain such priornowledge (e.g., the seizure duration may be associated with aeizure pattern).

.2. Comparison with other long-term EEG studies

The state-of-the-art performance of seizure detection can bevaluated within or between persons: patient-specific seizureetection or within-patient seizure detection (i.e., testing on sameersons, EEG segments of each person is available for training orptimizing detectors), across-patient seizure detection (i.e., test-

ng on unseen persons). This can be done by using LOOCV and crossalidation on multi-center datasets [41].The across-patient seizureetection has been proven to be much more challenging than theatient-specific detection. It is because that both manifestations

f seizure EEG and background EEG can vary dramatically across

ndividuals. The variability of EEG in ID people may be even largerhan non-ID people due to uneven brain development disorders.hese factors complicate the design of a generic seizure detector

ng and Control 49 (2019) 404–418

for the ID people. To compare the seizure detection performanceon non-ID people, we reviewed only studies that performed across-patient seizure detection on on long-term EEG recordings as shownin Table 6.

Table 6 shows that in general, the state-of-the-art performanceof the across-patient seizure detection is much worse than within-patient seizure detection studies, where sensitivities of near 100%were often reported. See a review in [49]. Secondly, a large vari-ance of performance can be seen across these studies. It suggeststhat the performance is mainly determined by the characteristicsof a particular EEG dataset, i.e., the subject population. In addi-tion, the low performance (a sensitivity of 38%) on the Europeanepilepsy dataset (216 patients, multi-center datasets) indicates thata generic seizure detector is still missing. In the last, we also listeda previous study using the same EEG dataset here as a comparison.However, our previous study is not an across-patient seizure detec-tion. Instead, it performed a seizure pattern-specific detection andfound that SVM achieved a better performance than LDA. This isbecause that all subjects’ data in our previous study were gatheredand randomized for a cross-validation testing. As a result, it mini-mized the heterogeneity of the EEG dataset by allowing a classifierto ‘see’ a part of EEG data of a testing subject.

4.3. Important factors and clinical relevance

Seizure visibility: The distinct seizures show significantly bet-ter detection performance than indistinct seizures. The occurrenceof indistinct seizures in this population may result from a com-bination of the EEG discharge pattern and EEG background. Suchcombinations have been found in spike-wave seizures [52] andnon-convulsive seizure status [53]. Furthermore, the indistinctseizures may also result from slow transitions between normaland epileptic activities [54]. In addition, the significant associationbetween seizure visibility and background indicates that the twofactors may have a common origin. However, not all ID patientsshow abnormal backgrounds and indistinct seizures. Around halfof the patients in this dataset show normal background activitiesand they tend to show more discharges with EMG activity, whichare relatively easy to detect.

Discharge patterns: The quantitative analysis of the surfaceEMG during epileptic seizures receives surprisingly little attention[28]. It seems to be a common practice to simply exclude all EMGactivities to reduce FD [42]. However, in this study, the seizuredetection shows desirable performance in patients with the seizurepattern of discharge with EMG activity. This finding agrees with ourprevious study that the EMG seizure epochs show the best clas-sification performance. EEG signals with contamination of EMG(or muscle activities) still contain information that discriminatesbetween epileptic and non-epileptic EEG discharge patterns (suchas EMG artifacts caused by chewing and other voluntary muscularcontractions) [55]. Given that 95% of the clinical seizures in this IDpopulation are motor seizures that show the EMG seizure pattern[9], we can expect a desirable performance of this seizure detectionmethod in a larger population.

In contrast, the seizure detection shows low detection perfor-mance in patients with the seizure pattern of the fast spike, whichagrees with the finding of our previous study that seizure epochsof fast spike show the worst classification performance. The fastspike often occurs at the seizure beginning with low-amplitudesignals, referred to as fast intracerebral EEG activity [56] or theelectrodecremental event [57]. The signals are significantly de-correlated during the fast spike seizures [57] and often have similar

frequency components with fast interictal EEG activities or EMGartifacts with low amplitude. Therefore, they are difficult to distin-guish from interictal EEG. The low performance of the spike-wavepattern may result from the high level of interictal epileptiform

L. Wang et al. / Biomedical Signal Processing and Control 49 (2019) 404–418 415

Fig. 16. Normalized output (PS) of classifier LDA and SVM on the same patient (Subj#16, around 24 h EEG). Ann is experts’ annotation showing the locations of 4 seizures (0:non-seizure, 1: seizure). The SVM (AUCPR = 0.55) achieves a better classification performance than LDA (AUCPR = 0.44) on this patient. The optimal DTs are: DT = 0.98 (LDA),and DT = 0.34 (SVM).

on EE

dcse

vftratachdgEbi

Fig. 17. Typical normal background of subject #16 with the Alfa activities

ischarges (IEDs) with a spike-wave pattern. Such spike-wave IEDsan occur during more than 50% of the entire EEG recordings inome individuals, while less than 1% in the intellectually normalpileptic patients.

EEG background: The definition of background abnormitiesaries in different studies, e.g., hemispheric symmetry, a lack ofaster activity [29], or the slow EEG in patients with ID. The defini-ion of background abnormity is also related to age and circadianhythm. For example, slow activities in the delta and theta bandre normal in children but considered abnormal in adults [58]. Inhis study, we specifically define the background abnormalities asbnormal (i.e., mostly slow and irregular) interictal EEG activitylassified according to its spatial distribution: focal (regional or oneemisphere) or bilateral generalized [59]. Results show that theetection performance tends to decline with the patients’ back-round abnormalities. Interestingly, ID patients often show similar

EG characteristics with neonatal EEG, and a strong association haseen noticed between intractability and abnormal EEG background

n childhood epilepsy [30]. ID patients have many seizure-like

G channels P3-Pz. Note the eye-blink artifacts on EEG channels Fp1-Pp2.

artifacts and artifact-like seizures that also were found often inneonatal EEG [60].

Wake/sleep status: Apart from the EEG factors, seizure detec-tion performance differs between wake and sleep status, i.e.,patients tend to show more FDs during wake than during sleep sta-tus. A better detection performance may be expected when we usesleep- or wake-specific classifiers. However, the actual applicationwill depend on reliable automated wake/sleep recognition meth-ods, which is still a challenge because of the lack of specific EEGmarkers of sleep in this population, such as spindles or K-complexes[23].

4.4. Possible applications for detection of NCSS

In clinical practice, long-term monitoring of EEG activity is

often essential to detect series of seizures or NCSS [16]. Suitableultra long-term (subcutaneous) EEG recording systems such as theReveal®, are still lacking but under development [61]. Automatedreal-time detection of seizures will become essential elements of

416 L. Wang et al. / Biomedical Signal Processing and Control 49 (2019) 404–418

with t

tohtpbt[lgwiadab

5

wbdbodwdft

Fig. 18. Typical abnormal background of subject #1

hese long-term EEG recording systems. In the clinical practicef ID patients, the NCSS often lasts long (several minutes up toours), with indistinct seizure boundaries due to a coexistence ofhe seizure and background state. The seizure detection methodroposed in this study could be used in the NCSS detection. First,ased on a well-annotated EEG dataset of NCSS, specific EEG fea-ures characterizing typical NCSS EEG, e.g., series of spike-waves19] should be developed and added. Second, since NCSS is oftenonger and shows a coexistence of EEG discharge patterns and back-round patterns, the firing power approach with a longer slidingindow (i.e., raising a seizure alarm when more than a half epochs

n a sliding window are seizure candidates) will be more appropri-te. Finally, given that the EEG factors have an effect on the seizureetection, the context information such as the context-based rulesnd other non-EEG physiological signals (e.g., heart rate/ECG) coulde used to improve the detection performance.

. Conclusions

The EEG signals of the ID population form a heterogeneous entityith respect to important factors: EEG discharge patterns, EEG

ackgrounds and EEG seizure visibility. The performance of seizureetection varies significantly with these factors. The abnormalackground and indistinct EEG seizures tend to occur simultane-usly and may have a common origin. The seizure detector showedesirable performance on a dominant seizure pattern: discharge

ith EMG activity, which indicates a promising result for a generic

etection in a larger population. A sensitivity of 100% was achievedor longer EEG seizures (>60 sec), making future automated detec-ion of NCSS (with longer duration) feasible.

he prominent slow activities in centre brain region.

Acknowledgment

We thank Prof. S. Van Huffel from KU Leuven for her insightfulcomments on this manuscript. We thank anonymous reviewers forthe constructive reviews. We also thank the colleagues in EpilepsyCenter Kempenhaeghe for the help on collecting and annotatingthe EEG dataset.

Appendix A. More detailed results

We compared several classifiers and chose the one with thegenerally best classification performance to perform seizure detec-tion and statistical analysis. Results of LOOCV show large varianceacross individuals (see Fig. 13). LDA achieved better performanceon a larger size of patients (AUCPR > 0 . 1, n = 13). More details aboutthese classifiers and optimal parameters refer to our previous study[13] (see Figs. 14–18).

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