Model-Based Seizure Detection for Intracranial EEG Recordings

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 59, NO. 5, MAY 2012

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Model-Based Seizure Detection for Intracranial EEG Recordings R. Yadav, Student Member, IEEE, M. N. S. Swamy, Fellow, IEEE, and R. Agarwal∗ , Member, IEEE

Abstract—This paper presents a novel model-based patientspecific method for automatic detection of seizures in the intracranial EEG recordings. The proposed method overcomes the complexities in the practical implementation of the patient-specific approach of seizure detection. The method builds a seizure model (set of basis functions) for a priori known seizure (the template seizure pattern), and uses the statistically optimal null filters as a building block for the detection of similar seizures. The process of modeling the template seizure is fully automatic. Overall, the detection method involves the segmentation of the template seizure pattern, rejection of the redundant and noisy segments, extraction of features from the segments to generate a set of models, selection of the best seizure model, and training of the classifier. The trained classifier is used to detect similar seizures in the remaining data. The resulting seizure detection method was evaluated on a total of 304 h of single-channel depth EEG recordings from 14 patients. The system performance is further compared to the Qu–Gotman patient-specific system using the same data. A significant improvement in the proposed system, in terms of specificity, is observed over the compared method. Index Terms—Automatic seizure detection, EEG, epilepsy, statistically optimal null filters (SONFs).

I. INTRODUCTION PILEPSY is a chronic condition of the brain characterized by an enduring propensity to generate epileptic seizures that are different for each individual and contribute to disability and impaired quality of life. It has significant economic implications in terms of healthcare needs, premature deaths, and lost work production [1]–[3]. The primary diagnostic tool in the epilepsy is the EEG, in which the epileptic seizures become apparent as rhythmic discharges, often coinciding with or at times even preceding the earliest observable changes in behavior. Seizure detection helps in the accurate diagnosis of epilepsy and may be used to warn or abort an ongoing seizure [4]. Since

E

Manuscript received July 29, 2011; revised November 12, 2011 and January 6, 2012; accepted February 6, 2012. Date of publication February 22, 2012; date of current version April 20, 2012. This work was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada under Grant A-7739. Asterisk indicates corresponding author. R. Yadav and M. N. S. Swamy are with the Center for Signal Processing and Communications (CENSIPCOM), Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada (e-mail: [email protected]; [email protected]). ∗ R. Agarwal is affiliated with the Center for Signal Processing and Communications (CENSIPCOM), Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TBME.2012.2188399

the time of seizure occurrence is unknown, the monitoring periods range from days to weeks generating a huge amount of data. Seizures are identified in the voluminous EEG data by visual inspection, which is time consuming, tiresome, and prone to errors. Thus, automatic seizure detection is warranted to aid in the rapid review of the voluminous EEG data [4], [5]. One popular design approach for automatic seizure detection is based on determining the boundaries between seizure and nonseizure EEG features by complex discrimination systems in which large training data are utilized. Detection techniques developed by this approach are commonly referred to as generic seizure detection methods, and perform poorly due to the high inter-individual EEG variability [5], [6]. It is generally considered difficult to design a single method that can detect all types of seizures in all patients [7]. The recurring nature of seizures within a patient opens possibilities to overcome the poor performance of the generic detectors. It is observed that in most of the patients, one or two and sometimes more types of seizures tend to occur repeatedly. In these cases, the electrographic seizure activity within each type is similar, though not identical [8]. It is possible to train a patient-specific seizure detector utilizing the information extracted from a previously identified template seizure for the patient under consideration. Detection schemes based on this design approach report significantly improved detection performance over the generic schemes [8]–[10]. In addition to the improved detection performance, patient-specific techniques play a pivotal role in defining and understanding the epileptogenic area that can possibly lead to improved surgical treatment [2], [6], [11]–[15]. Even though the patient-specific schemes demonstrate improved performance over the generic seizure detection methods, they are not practical. The main limiting factors in all patientspecific seizure detectors are 1) supervised selection of the template seizure; 2) supervised selection of the nonseizure EEG (or a set of nonseizure EEG patterns); and 3) supervised training of the classifier. Another fundamental problem in seizure detection methods (generic as well as patient specific) is in detecting seizures that have subtle changes in the amplitude [4], [5], [10]. This problem is also evident in the visual detection of seizures. Addressing some of these limitations will lead to a more practical solution in the design of patient-specific seizure detectors. We present a novel method that addresses two of the three aforementioned limitations, notably, the supervised selection of nonseizure EEG and supervised training of the classifier. Moreover, our method is capable of accurately detecting seizures that evolve with subtle changes in the EEG amplitude. Initial results of parts of the overall method have been presented at the IEEE

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Fig. 1. Illustration of temporal evolution of seizure on a single-channel EEG that is observed as changing piecewise stationary rhythms as seizure continues.

meetings [16]–[18]. The method operates in two modes: training and testing. In the training mode, the system builds the seizure model and trains the classifier. In the testing mode, the classifier detects seizure similar to a priori known seizure by tracking and matching the time evolution of the seizure. The time evolution of the seizure is the sequence of occurrence of the piecewise stationary rhythms in the seizure. This novel approach allows the accurate identification of recurring seizures and significantly reduces false detections due to artifacts and nonepileptic rhythms. Furthermore, the proposed system performance is compared to the Qu–Gotman patient-specific system [8] using the same data. The remaining paper is organized as follows. Problem statement is described in Section II. Section III presents the modelbased seizure detection system. Section IV presents the result from the proposed system on 14 patient data and is compared to the popular Qu–Gotman patient-specific system [8]. Results are discussed in Section V and conclusions are drawn in Section VI. II. PROBLEM STATEMENT We assume seizure to be a narrowband signal in comparison to the disproportionately large background EEG and define the problem as the detection of rhythmic narrowband seizure activity, s(n), from the observed EEG consisting of signal and noise, x(n) = s(n) + n(n). It is important to note that the rhythmic narrowband seizure activity evolves as the seizure progresses over time. In other words, a seizure is composed of short piecewise stationary rhythmic discharges that change from one rhythm to another as the seizure evolves [8], [13], [14], [19]. Fig. 1 illustrates the temporal progression of a seizure in short piecewise stationary rhythms. The problem can be redefined as the detection of these rhythms, i.e., the template seizure s(n) consists of a set components, s1 (n), s2 (n), . . . sN (n), with a specific order of temporal occurrence. We propose a model-based seizure detection system using the statistically optimal null filters (SONFs). SONF is a novel approach for solving the problem of enhancement or suppression of narrowband signals of short duration by combining the maximum signal-to-noise (SNR) ratio and least-squares (LS) optimization criteria [20]. Its intrinsic property is the ability to

track signals rapidly leading to a more practical processing of short-duration signals and has been shown to be equivalent to the well-known Kalman filter, but with a much simpler implementation [21]. Since the SONF is a linear time-varying filter, it can be implemented as a set of N parallel branches—one to estimate each seizure component without significantly increasing the computational cost. Parallel SONF branches become advantageous to track the temporal occurrence of the individual seizure components that can further improve the detection specificity. That is, the false detections due to nonepileptic rhythmic discharges that have intrinsic characteristics similar to some of the seizure components can be minimized. This is possible because an epileptic seizure is observed to evolve with sustained dominant rhythms in short bursts as shown in Fig. 1, while normal EEG rhythms do not typically evolve [2], [17]. SONFs require a priori knowledge of the signal components or the basis functions constituting the signals to be estimated. III. METHOD A. Data Selection The intracranial EEG data used in this study were acquired with the Harmonie System (Stellate System Inc., Montreal, Canada) from the Epilepsy Telemetry Unit at the Montreal Neurological Institute and Hospital (MNI/MNH). The data were filtered between 0.5 and 70 Hz, prior to digitization at the sampling rate of 200 Hz. All patients had stainless steel depth EEG electrodes of nine contacts that were surgically placed inside the brain. There was no prescreening of the patients other than the requirement that they had at least three electrographic seizures during the monitoring sessions. For each patient, five sections of recordings, approximately 4–7 h each, were extracted such that three sections had at least one seizure each, one section during wakefulness without seizures, and one without seizures during sleep. Prior to sectioning, a trained EEG specialist using a bipolar montage scored all data for seizures. Since in some patients, seizures are only present in a single channel (focal seizures), we considered a single-channel evaluation of the patient data. Visual inspection of the first seizure section facilitated the selection of the single channel for analysis. For a patient with seizures occurring simultaneously on the multiple channels, we selected the channel in which the seizure is the most prominent. The selected channel is used to evaluate all data for the given patient. Data from 15 patients, originally collected between August 1998 and June 2004 for another study [5], were considered for the design and development of the proposed algorithm. Data of one patient were rejected because it was not possible to clearly define the onset and the end of seizures. Thus, the data from 14 patients with over 304 h of EEG are used in this study. For each patient, the first occurring seizure in one of the three seizure sections is used as the template seizure pattern (TPAT ) for the training of the proposed system. TPAT is the first 60 s or the complete seizure if it lasts less than 60 s. The training data automatically include 30 s of EEG preceding the seizure template.The trained detector is used to detect all other seizures for the given patient. In the literature, there exists a plethora of patient-independent seizure detection methods while only a

YADAV et al.: MODEL-BASED SEIZURE DETECTION FOR INTRACRANIAL EEG RECORDINGS

Fig. 3. Fig. 2.

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Instantaneous matched filter.

Block diagram of the proposed model-based seizure detection system.

handful of patient-specific methods. The majority of patientspecific methods inherit concepts from the Qu–Gotman patientspecific detection scheme [8]. Some of the more recently proposed patient-specific techniques require two or more examples of the template patterns to train the classifier. On the contrary, the Qu–Gotman method requires a single template pattern to train the classifier. Considering some of these factors, we selected the Qu–Gotman system for comparative performance evaluation. The template pattern (multichannel) for the Qu–Gotman system is the first occurring seizure for the given patient. The method additionally requires a minimum of 30 min of nonseizure EEG to train the classifier. We selected 30-min section of nonseizure EEG preceding the template pattern. For both methods, the training data consisted of the first seizure in each patient (14 template seizures) and the test data included the remaining 4 sections of data for each of the patients consisting of 68 seizures in the 304 h. We used the hold-out validation technique in evaluating the performance of the proposed and Qu–Gotman systems. Hold-out validation avoids overlap between training and test data, yielding a more accurate estimate for the generalization of the performance. B. Model-Based Seizure Detection In the proposed method, the first step involves partitioning the template seizure signal (TPAT = s(n)) into set of piecewise stationary seizure components or epochs, i.e., s(n) = {s1 (n), s(n), . . . , sN (n)}. The second step involves extracting “k” nonredundant components of TPAT from the set s(n) referred to as “template epochs.” The third step involves identifying the basis functions for each template epoch required to implement the SONF. The seizure model is a set of orthogonal basis functions for each of the k-template epochs. Finally, the classifier is trained using TPAT and the derived seizure model. The trained system is employed to detect narrowband template epochs in all subsequent data. As mentioned earlier, tracking of the time-ordered occurrence of k-template epochs is possible by the parallel implementation of the SONFs where one SONF corresponds to one of the k-template epoch of TPAT . That is, at any given time only one SONF will track a template epoch. By tracking the time-ordered estimation of components by the SONF, it is possible to identify the subsequent seizures with similar characteristics. The block diagram of the model-based seizure detection system is shown in Fig. 2. The building blocks of the proposed system are 1) preprocessing and artifact rejection; 2) seizure model; 3) SONF; 4) detection criterion (energy ratio); and

5) evolution-based classifier. The following describes each block of Fig. 2 in detail. 1) Preprocessing and Artifact Rejection: Artifacts of cerebral, noncerebral, and environmental origin often corrupt the scalp EEG. Intracranial EEG, on the other hand, is relatively free from artifacts in comparison to the scalp EEG, but spans wider frequency spectrum, highly variable seizure morphology, and variety of sharp wave complexes, ranging from needle-like fast activity to much slower discharges that can be contaminated by fast electromyography (EMG) activity [2]. EMG artifact in the intracranial EEG is observed with substantial energy in the spectral content beyond 30 Hz [22]. Generally, most of the seizure activity is reported to be within 30 Hz [19], [23]. Therefore, we employ a fifth-order Butterworth low-pass digital filter (cut-off frequency fc = 30 Hz) to remove unwanted high-frequency interferences. Amplitudes in the subdural EEG recordings are typically in the ±2500 μV range. The sections of EEG where the activity exceeds ±2500 μV are potentially due to amplifier saturation and are thus, ignored from further processing. Since the SONF has properties similar to the recursive leastsquares (RLS) estimation technique, it is expected that it may not work well for data that contain randomly occurring data points with extreme values. That is, the tracking capability of the SONF may be impaired or reduced in the presence of sporadically occurring high-amplitude transients. Such instances of the highamplitude transients need to be suppressed before estimating the desired signal by the SONF. We reuse the idea of instantaneous matched filter (IMF) proposed by Agarwal et al. [24], [25] that forms a key building block in the SONF to identify and suppress instantaneous time points of high-amplitude transients within each processing epoch. If a matched filter (MF) is used to detect the signal at any given time, then at the output, we obtain a signal that provides the maximum output signal-to-noise ratio (SNRo ), for the considered time interval −0 to n. Since the time interval or frame of observation is continually increasing, at each considered time instant, the MF provides a new output signal and a new SNRo , and hence it is termed as the IMF [24]. Fig. 3 depicts the IMF. Note that the limits of integration is a time variable n. The IMF provides at each instant of time the maximum SNRo at the output, v(n), and the effect of sporadically occurring high-amplitude transients on v(n) is seen as sudden jumps or shifts that must be suppressed prior to estimation. To do so, each test epoch (x(n)) is screened for high-amplitude transient artifacts and the corresponding sample points are attenuated. This nonlinear filtering is achieved by analyzing the output of the IMF. The transients in the EEG [circled in Fig. 4(a)] are seen

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Fig. 4. Impact of high-amplitude transients on the tracking ability of the SONF. (a) Estimate sˆ(n) from the observed signal x(n) using SONF in the presence of transient (enclosed in the ellipse). (b) Estimate sˆ(n) of the observed signal x(n) after filtering the observed transients. (c) IMF output (v k (n)) of the kth branch of the SONF for the two cases.

as sudden jump/shift in the output of the IMF (vk (n)) [circled in Fig. 4(c)] of the kth branch of the SONF. The filtering process involves first removing linear trends in v(n). This is achieved by applying the first difference operator. Let V be detrended v(n), a random variable of unknown distribution with expected mean μ and variance σ 2 . Then for any real number b > 0, Chebyshev’s inequality is defined as P (|V − μ| ≥ bσ) ≤ b12 . Assuming that a relatively small percentage of outliers (high-amplitude transients) are contained in x(n), then outliers are the sample points outside the boundary, bσ, i.e., sample points 3σ (where b = 3) away from the mean are considered to be due to the high-amplitude transients [17], [26]. The amplitudes of the corresponding sample points are attenuated by the empirically determined factor of 75% [17]. The ˆ are shown in Fig. 4(b), which filtered x(n) and its estimate s(n) clearly reflect suppression of the high-amplitude transients. The filtered x(n) is processed by the SONF for seizure detection. 2) Seizure Model: In the training mode for each patient data, the proposed system develops a seizure model for the a priori known template seizure. The fully automated process of modeling TPAT involves 1) segmentation of TPAT into piecewise stationary segments; 2) identification and rejection of redundant epochs resulting from the segmentation of TPAT ; and 3) modeling of each remaining template epoch. STFT-based segmentation: In the original work [16], the template pattern was visually segmented into 6 s epochs. As a fixed length of the epochs may not be ideal for all types of seizures, we determine the length of the epochs using adaptive segmentation. The process is automated by introducing a short-time Fourier transform (STFT)-based segmentation technique to identify the stationary sections [18]. The Fourier transform of the template pattern is computed for sliding data frames of 2 s with a step size (τ ) of 0.25 s. The dominant rhythm frequency Fm in each data frame is defined as the frequency with maximum power: Fm = max{X(f )} f

(1)

where X(f ) is the discrete Fourier transform and m denotes the maximum power. The resulting set is a new discrete time

Fig. 5. STFT-based segmentation of T PAT . (a) 60 s long T PAT is shown along with the evolution of dominant (peak) frequency Fm in (b). The epochs identified by the adaptive segmentation algorithm in T PAT are enumerated.

series of the dominant frequency with a sampling interval of τ as shown in Fig. 5(b). The changes in the dominant rhythm of TPAT are observed as a change in Fm (τ ). The points of change in the dominant frequencies are identified as segmentation boundaries given by gτ = |Fm (τ ) − Fm (τ − 1)| < Δ

(2)

where Δ(= 0.25) is the tolerance threshold defined as the maximal allowable change in two consecutive Fm samples [18]. Segmented epochs shorter than 2 s are rejected. By doing so, we retain only the epochs with sustained dominant rhythm. The length of the basis functions is set as the average duration of all the template epochs. The length of the sliding test window required in the SONF is set equal to the length of the basis function. In the example of Fig. 5, TPAT is segmented into a set of seven stationary epochs (p = 7), E = {E1 , E2 , . . . , E7 } based on constant dominant rhythm frequency, Fm . Some epochs ({E1 , E2 }, {E3 , E4 }, and {E5 , E6 , E7 }) in this set represent the same dominant rhythms. It is also possible that some of the epochs may be due to noise. Such epochs must be eliminated prior to the construction of the seizure model. Rejection of artifacts and redundant epochs in TPAT : A simple approach to keep one of the several epochs with the same dominant rhythm is possible by examining the dominant frequency. Alternatively, cross-validation techniques can be explored to identify and reject redundant epochs. We use the latter to identify and reject redundant epochs. The idea is to use the pth template epoch to derive a model for use in the SONF to process the remaining p − 1 template epochs. The detection threshold is set by running the pth epoch through the newly derived SONF and is one-third of the maximum of energy ratio (γp ) computed by taking the energy ratio of the estimated and input epoch (pth). From the remaining p − 1 epochs, all epochs detected by the pth model are observed to be similar. The epoch with the highest energy ratio is retained as one of the template epoch to model the seizure. The process is repeated until all epochs are unique. Epochs due to noise consist of a mixture of frequencies without any sustained rhythm. As a result, the detection threshold obtained using the corresponding model, and the

YADAV et al.: MODEL-BASED SEIZURE DETECTION FOR INTRACRANIAL EEG RECORDINGS

Fig. 6. Estimation of a signal using the SONF. (a) Block diagram of k-branches of the SONF. (b) Estimation counterpart of the kth discrete SONF.

epoch, is observed close to zero. Thereby, the model detects all p − 1 epochs. Clearly, this is not possible as the seizure evolves with sustained dominant rhythm. Such epochs due to noise are excluded from further considerations [17]. The resulting k template epochs are disjoint and noise free that are utilized to build the final seizure model. As seen in the example of Fig. 5, the segmentation of TPAT resulted in seven stationary epochs (E = {E1 , E2 , . . . , E7 }) of which only three are unique (k = 3). These three disjoint template epochs are utilized to build the final seizure model that uses k-SONF branches, one SONF for each template epoch as shown in Fig. 6(a). Modeling of the TPAT epochs: The SONF requires an orthogonal linear expansion of the signal to be estimated. The basis functions in this expansion constitute the model for the signal. We, therefore, consider representing EEG rhythms (template epochs) in terms of linear combination of sinusoids which is one of the more popular approaches employed for the analysis of signals. One approach of constructing basis functions to model the template epochs in the wavelet domain is proposed in [16]. In this method, each template epoch is decomposed into wavelet scales in the 3–25 Hz band. At the sampling rate of 200 Hz, scales 3, 4, and 5 correspond to the 3–25 Hz band. The scale contributing maximum energy is selected and the peak frequency in the spectrum of the selected scale signal is used to construct the sinusoid. Since the phase information is unknown, the Hilbert transform is also required. This approach of constructing the basis functions is limited by the sampling rate

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at which the EEG is digitized. We introduce three additional techniques of modeling the template epochs [16], [18]. 1) Sinusoidal basis function (SBF): The first and second dominant frequencies corresponding to the two largest peaks in the power spectrum of the template epochs are selected and modeled by sinusoids. Hilbert transforms (quadrature component) of the sinusoids of the selected frequencies are also considered since the phase of the input is unknown. 2) Harmonic basis function (HBF): It is observed that power spectral density of some template epochs consists of dominant peaks as well as their harmonic components. As with the SBF method, the first and second dominant frequencies corresponding to the largest peaks in the spectrum are selected to formulate the basis functions. Additionally, the harmonics of the two dominant rhythms are also identified. Sinusoids corresponding to the frequencies of the two dominant rhythms and their relevant harmonic components (and their Hilbert transform) are used as the basis functions to model each template epoch. 3) Ratio-spectrum basis function (RBF): In this approach, the ratio of the power spectral density of the template epochs and background EEG is taken to highlight dominant seizure frequencies [27]. The dominant frequencies resulting from the ratio spectrum are selected and modeled using the SBF approach. The reference EEG is the 30 s of background EEG preceding the template seizure pattern. Of the three, the model that best represents TPAT is selected to identify similar seizures in the remaining data. The selection of the best model representing TPAT is described in the classifier training section. The main building block of the model-based seizure detection is the SONF, as described in the next section. 3) Statistically Optimal Null Filter: SONF is a novel nonparametric approach first proposed by Agarwal et al. [20] for enhancement/suppression of narrowband signals based on combining the maximum output SNR and the LS optimization criteria. SONFs are obtained by optimally scaling the output of the IMF. Its intrinsic property is the ability to track signals rapidly leading to a more practical processing of short-duration signals. SONFs have also been shown to be equivalent to the well-known Kalman filter without requiring the solution of nonlinear equation of the Ricatti type that is essential in computing the gain of the Kalman filter [21]. Fig. 6(b) shows the implementation of the SONF for the kth template epoch. In SONF-based estimation of a signal with unknown shape, we assume that the desired signal sk (n) can be represented as a linear combination of a priori known set of orthogonal basis function, (Φk (n) = {φ1k (n), φ2k (n), . . . , φN k (n)}), i.e., sk (n) =

N 

aik φik (n)

(3)

i=1

where aik are the unknown scaling variables and sk (n) is the kth epoch of TPAT . The output of the IMF vki (n) is scaled by λik (n)s (obtained through LS optimization) to produce the estimate of

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between nonseizure and seizure EEG in the SONF framework. To do so, the distance (Euclidean) between the energy ratio of template epoch and background EEG is computed. The Euclidean distance dlp for the lth modeling technique for the kth template is given by  ⎞  ⎛ Nb k g Nk   bkg 1  1  Sez γ (n) − ⎝ γ (n)⎠ (6) dlk =  Nk n =1 lk Nbkg n =1 lk

Fig. 7. Evolution-based classification. (a) Two disjoint (k = 2) template epochs of T PAT . (b) Power spectral density plot of the template epochs. (c) Example of event detected similar to T PAT . (d) Energy ratios γk for the kbranches of the SONF (one model for each template epoch) of the detected pattern. (e) Detection by the individual models (M 1 and M 2 ) and combined detection sequence (M d ) for all models. The “number” represents time order in which the template epochs constitute T PAT = {E 1 , E 2 } that is examined by the evolution-based classifier to make a detection. The vertical “dashed” line denotes the detections.

the desired template epoch [see Fig. 6(b)]: sk (n) =

N 

λik (n)vki (n).

(4)

i=1

A detailed description of the statistical optimal null filter can be found in [20]. The data are processed in short segment (test epoch) that slide in a small step (step size = 0.25 s). The epoch length ranges from 2 to 6 s, determined by the segmentation algorithm [18]. A small step size allows tracking of subtle changes in the EEG. 4) Energy Ratio: Each template seizure pattern contains kdisjoint unique template epochs that occur at specific time in TPAT . Each template is modeled as described previously. The training of the classifier involves setting a threshold for the detection of each of the template epochs. Since the SONF estimates the desired signal using the model, the output of the SONF is nearly zero except when the input EEG matches the model. The energy ratio γk of the kth estimated component and the input signal can be used to discriminate parts of the seizure that are similar to the kth template epoch, and is given by (see Fig. 7)  2 sk (n) (5) γk =  2 x (n) and the detection threshold for the kth model δkth is set as 1/3 of the maximum γk . The strategy for the selection of δkth , described in [16] and [18], was based on the observation in simulated EEG and five patients’ EEG data. Model selection: Each template epoch can be modeled using the three techniques (l = 3) described previously. Model selection involves selecting the model that best represents the template epoch, i.e., the model which results in maximum separation

where Nk is the length of a template epoch, Nbkg is 30 s of the bkg Sez and γlk are the energy ratios of the background EEG, and γlk kth template epoch and the background EEG obtained using the lth modeling technique. Among three models for the kth template epoch, the one that results in maximal separation between seizure and nonseizure segments quantified by the metric dlk is considered. 5) Evolution-Based Classifier: Existing patient-specific seizure detection systems in the literature identify patterns similar to the template pattern as well as false events. The false detections are due to artifacts and nonepileptic rhythms that reduce the overall detection specificity of the method [8]–[10]. The detection specificity can be improved by tracking the timeordered sequence of the occurrence of template epochs that constitute TPAT . For a given TPAT , the modeling step resulted in k-disjoint, nonredundant, noise-free template epochs. The sequence of their occurrence is remembered and matched with the time-order sequence of the candidate seizure pattern. This matching of time sequence within a given time frame is what we define as the evolution-based classification. A seizure similar to the template is detected when the time sequence of the epochs matches that of TPAT epochs within a 60 s time frame. Fig. 7 illustrates this novel evolution-based classification approach employed in the proposed model-based system. In this example, the modeling process resulted in two disjoint nonredundant epochs for the given TPAT which are labeled E1 and E2 as shown in Fig. 7(a). The enumerated subscript denotes the time order in which they occur in TPAT . The power spectral density plot confirms the nonoverlapping dominant rhythm of the template epochs [see Fig. 7(b)]. The model-based PSA system for this TPAT consists of two parallel branches of SONF, one for each template epoch. The detection thresholds are set using the model and training data as described in the classifier training section. The trained PSA system is utilized to detect candidate seizure patterns similar to TPAT . An example of the detected CPAT similar to TPAT is shown in Fig. 7(c). Raw EEG is shown to map the detection by the evolution-based classifier. The energy ratios (γ1 , γ2 ) are shown in Fig. 7(d). The time sequence of the detected epochs in CPAT is matched to the template epochs of TPAT to make final detection. In this example, the sequential detection of template epoch 1 followed by template epoch 2 matches the sequence of the template epochs in TPAT at two different instances. These are represented by vertical dashed lines labeled detection 1 and detection 2. Detection 1 occurs because template epoch 1 is immediately followed by the detection of template epoch 2. In detection 2, there is a gap

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TABLE I DETECTIONS OF THE PROPOSED AND QU–GOTMAN SYSTEMS

between template epochs 1 and 2. Since these two epochs are detected in the correct order within the 60 s detection criterion, they qualify as valid seizure detection. C. Performance Evaluation Prior to any evaluation, all detections within 30 s of each other are combined as a single final detection. The performance of the proposed model-based seizure detection system is assessed on intracranial EEG data that were originally scored based on all channels for another study [5]. We selected the most prominent seizure channel from the multichannel EEG of an individual patient for evaluating the performance of the proposed system. An event detected within 60 s of the manually scored seizure onset is considered a good detection in the proposed system. We evaluate the Qu–Gotman system [8] using the same dataset and the same criterion. The performance is assessed by three popular measures in the seizure detection literature: sensitivity, specificity, and false detection rate (FDR). Sensitivity is defined as the percentage of expert labeled events detected by the algorithm, and specificity is defined as the percentage of seizure events detected by the algorithm that are true positives. This is consistent with what has been reported in the seizure detection literature [5], [28]. Note that our definition of the term “specificity” is also known as positive predictive value or accuracy in the diagnostic testing [28]. FDR is defined as the number of false detections per hour. IV. RESULTS As described earlier, the first occurring seizure in each patient is considered as the template pattern for both Qu–Gotman system and our system. The background EEG for our system is 30 s of the EEG preceding the template pattern that is automatically

selected. In the case of Qu–Gotman system, continuous or a set of the seizure-free EEG totaling 30 min is manually selected preceding the template pattern. The background EEG in some patients included one or two patient disconnection sections. The background EEG is visually inspected for disconnection sections. When the background EEG contains patient disconnections, additional seizure-free EEG data are added to compensate the disconnection section. Both systems were trained using the template pattern and background EEG for each patient data. Results of the proposed and Qu–Gotman systems for the individual patients on the test data are shown in Table I. Our system did not make any false detections while missed seizures in Patients 3, 4, and 10 resulting in an overall 100% specificity (FDR = 0/h) and 92.2% sensitivity. The Qu–Gotman system missed seizures in Patients 3 and 7, and made false detections in all except Patients 2, 7, 8, 13, and 14 resulting in an overall specificity of 66.6%, a sensitivity of 93.7%, and an FDR of 0.2/h. The proposed system shows a significant improvement (approximately by 33%) in the specificity when compared to the Qu–Gotman system though at the cost of 1.5% drop in the sensitivity. V. DISCUSSION The proposed model-based seizure detection system aimed to 1) improve the overall detection specificity and 2) address some of the complexities in the practical implementation of the patient-specific seizure detection approach. Our system significantly improved the detection specificity where no false detection was reported, while the Qu–Gotman system reported an FDR of 0.2/h on the same dataset. The FDR of other patientspecific seizure detectors in the literature are reported to be in the range of 0.02 to 0.5/h [8]–[10], [29]. Clearly, our method outperforms these patient-specific seizure detectors.

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Fig. 8. Template pattern and a missed seizure by the proposed model-based seizure detection system in Patient 3 (channels: RC1–RC3).

Although the patient-specific seizure detectors show improved sensitivity and FDR over the generic methods, they are not practical. The main limiting factors are 1) selection of the template seizure pattern; 2) selection of the background EEG; and 3) supervised training of the classifier. We overcome these challenges in the patient-specific seizure detection approach by using a novel model-based scheme with the SONFs as the building blocks. The SONF is a simplified implementation of the Kalman filter and can track narrowband signals buried in noise [20], [21]. The idea is to detect short-duration dominant rhythms of the seizure for which SONFs become practical. Adaptive modeling and unsupervised classifier training are some of the key attributes of this new system. Unlike the multiresolution-based detectors, our system is not limited by the sampling rate. Additionally, seizures with minimal change in the EEG amplitude are reported to be generally very difficult to detect by experts as well automatic seizure detectors. The proposed method is capable of detecting such seizures. We studied the proposed system’s ability to detect low-amplitude seizures using simulated data in [17]. Patient 7 is one such example that had very low amplitude seizures, and low amplitude is one possible reason for missing seizures in this patient by the comparison system. The proposed system resulted in no false detections while missing five out of nine seizures in Patient 3, one out of three seizures in Patient 4, and one out of five seizures in Patient 10. An example of the template pattern and a missed seizure by the proposed system for Patient 3 (channels: RC1–RC3) is shown in Fig. 8. The initial few seconds of the template pattern contains the mixed frequency characteristics (4–34 s) that later evolve into rhythmic activity. The missed seizure is similar to the mixed seizure part of the template seizure in the initial few seconds (6–20 s), but did not continue to evolve into the rhythmic part. On careful examination of the four derived template epochs for these patient data, it is observed that the first template epoch corresponded to the mixed frequency part, while the other three template epochs came from the rhythmic part of the template pattern (TPAT ). The proposed system is

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 59, NO. 5, MAY 2012

designed to detect seizures when the candidate seizures match the time-ordered occurrence of the template epochs in TPAT . All missed seizures of this patient did not meet this criterion. Clearly, the missed seizure does not match the template seizure. Since the first 20 s of all seizures in this patient were very similar, we tested the idea of using only the first 20 s of the template pattern to build the seizure model. However, this was not successful as a minimum of two disjoint template epochs could not be found. Our method is designed to detect seizures evolving with sustained dominant rhythms. Therefore, it is not a surprise that the seizures that do not evolve with sustained dominant rhythms were not detected. In contrast, the Qu–Gotman system detected majority of the seizures for this patient but at the cost of 11 false detections. Similarly, the missed seizure in both Patients 4 and 10 did not match the evolution pattern similar to the template seizure pattern. The Qu–Gotman system detected all seizures in Patient 4 at the cost of several false detections resulting in 100% sensitivity and at the cost of much lower specificity (25%). The Qu–Gotman system detected all seizures in Patient 10 with no false detection. On the other hand, the Qu– Gotman system missed seven out of nine seizures in Patient 7 while our system detected all of them with no FDs. The likely cause for missing seizures in this patient is the very low EEG amplitude. Additionally, the missed seizures by the Qu–Gotman system did not evolve similarly to the multichannel template seizure within first 20 s. The epileptic discharge propagation through the brain structures may not be precisely reproducible phenomena at all the times [13], [30]. The seizures can go undetected by the Qu–Gotman system due to spatial and temporal constraints which require the seizure onsets to occur in the same channel as those of the template seizure [8]. An example of training data and a missed seizure by the Qu–Gotman system is shown in Fig. 9. Clearly, in this example, the test seizure in channels other than RH1–RH3 was of low amplitude and did not evolve in the same manner as the template seizure. Since there are no spatial constraints in our system, it was capable of detecting low amplitude seizures. Majority of false detections made by the Qu–Gotman system were brief rhythmic bursts (