Epilepsy is the fourth most common neurological disorder

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1 High Performance EEG Feature Extraction for Fast Epileptic Seizure Detection Ramy Hussein, Mohamed Elgendi, Rabab Ward and Amr Mohamed Electrical and Computer Engineering Department, University of British Columbia, Vancouver, BC, Canada. Department of Computer Science and Engineering, Qatar University, Doha, Qatar. Abstract Epilepsy is a neurological disorder that affects around 7 million people worldwide. Early detection of epileptic seizures has the potential to help patients in improving their quality of life. Electroencephalogram (EEG) has been used to record the brain s electrical activities associated with seizures. This paper presents a fast method for selecting EEG features that are relevant to early detection of epileptic seizures. The feature extraction model is based on LASSO regression and is applied to the EEG spectrum to recognize the EEG spectral features pertinent to seizures. These features are then selected and fed into a random forest (RF) classifier for epileptic seizure recognition. Compared to the state-of-the-art methods, the proposed scheme achieves the highest detection performance of % sensitivity, % specificity, % classification accuracy, and.8 Sec detection delay. Furthermore, our model has proven to be robust in noisy and abnormal conditions. Index Terms EEG signals, epileptic seizure, LASSO regression, coordinate descent, Random Forest. I. INTRODUCTION Epilepsy is the fourth most common neurological disorder affecting around 7 million people worldwide []. The defining characteristic of epilepsy is recurrent seizures that strike without warning. Symptoms may range from brief suspension of awareness to loss of consciousness, and sometimes, violent convulsions []. Early detection of epileptic seizure has the advantage of warning patients of impending seizures so they can administer the appropriate medications on time. Studies of epilepsy often rely on the electroencephalogram (EEG), which indicates the brain s electrical activities associated with seizures [3]. EEG-based seizure detection systems aim at effectively analyzing the captured EEG data to accurately recognize epileptic EEG patterns. Numerous methods for epileptic seizure detection have been presented in the literature [] []. In [], the wavelet transform was used to obtain and analyze the main spectral rhythms of the EEG signals. Then, statistical features that characterize the behavior of the EEG were extracted and tested using a multilayer perceptron neural network (MLPNN). The results showed sensitivity, specificity, and classification accuracy of 9%, 9%, and 9.83%, respectively. In [5], a feature extraction method based on the sample entropy was used together with the extreme learning machine (ELM) classifier and resulted in sensitivity, specificity, and classification accuracy of 97.%, 98.77%, and 95.7%, respectively. In addition, the authors of [] used a combination of EEG time-domain features and spectral features, forming a more representative feature vector; this was then fed into a MLPNN for EEG classification. The epilepsy recognition rates achieved by this method were 97.% for sensitivity, 98.7% for specificity, and 97.5% for classification accuracy. In an attempt to decrease the computational complexity of seizure detection systems, the authors of [7] utilized a low-complex support vector machine (SVM) classifier along with the traditional wavelet features. This method achieved an average classification accuracy of 95.33%. A comparable classification accuracy of 95.% was achieved in [8] by using a novel EEG feature extraction method, while the features were extracted on the sensor side and seizure detection was performed on the server side. Furthermore, a feature extraction method based on the Hilbert transform was employed together with an SVM classifier to detect the existence of epileptic activities [9]. It achieved a classification accuracy of 97.%. Also, independent component analysis (ICA) was used to determine the independent seizure-associated features []. It was used with the SVM classifier to achieve a sensitivity, specificity, and classification accuracy of 9.%, 9.%, and 95.%, respectively. In [], a seizure detection scheme based on some statistical features and a least-square SVM (LSSVM) classifier showed an average accuracy of 97.9% with a short computation time of.5 seconds. In [], the energy features of EEG-based harmonic wavelet packet transform and fractal dimensions were computed and tested using a relevance vector machine (RVM) classifier to obtain a high classification accuracy of 99.8%. In an effort to further improve the performance of automatic seizure detection systems, we apply a low-complex feature learning model. This model extracts the most representative seizure-associated features in a computationally efficient manner, using the least absolute shrinkage and selection operator (LASSO). These learned features are proven to be more discriminative and achieve higher seizure detection accuracy than other hand-crafted features. Meanwhile, a computationallyfast algorithm, known as coordinate descent [], is used for estimating the LASSO regression coefficients, which are then used as features for seizure detection. The random forest (RF) classifier is used to examine the efficiency of the extracted features. The results on a benchmark dataset show that our approach surpasses existing methods in terms of sensitivity, specificity, classification accuracy, and latency. Finally, we show that the proposed model is robust against common EEG artifacts (eye-blinking and muscle artifacts) /7/$3. 7 IEEE 953 GlobalSIP 7

2 (a) (d) 5 (a) 5 (d) (b) (e) (b) (e) (c) (f) (c) (f) Fig.. Time-series EEG plots: (a), (b), and (c) clean EEG examples of normal, inter-ictal, and ictal activities, respectively; (d), (e), and (f) noisy EEG examples of normal, inter-ictal, and ictal activities, respectively. Fig.. Frequency spectrum plots: (a), (b), and (c) clean EEG spectrum of normal, inter-ictal, and ictal activities, respectively; (d), (e), and (f) noisy EEG spectrum of normal, inter-ictal, and ictal activities, respectively. A. EEG Data II. MATERIALS The proposed method was tested on the EEG dataset provided by Bonn University [3]. In this study, we address the classification problem between three different EEG sets, as follows: Normal EEG recorded from five healthy volunteers, Inter-ictal EEG recorded from five epileptic patients during seizure-free intervals and Ictal EEG recorded from five epileptic patients with active seizures. Each set includes singlechannel EEG signals, each of 3. Sec duration. All the EEG sets are denoised, amplified, sampled at 73. Hz and digitized using a -bit analog-to-digital converter. Figures (a), (b) and (c) show examples of noise-free EEG signals for normal, interictal and ictal activities, respectively. In practice, measured EEG data are often corrupted with different types of noise. Such noise negatively alters the EEG waveform shapes and severely affects the detection accuracy of epileptic seizures. In this study, we address the classification problem of (i) clean EEG data and (ii) noisy EEG data corrupted with eye-blinking and muscle artifacts. As demonstrated in [], eye blinks are modeled using random noise band-pass filtered between and 3 Hz, while the muscle artifacts are modeled using random noise band-pass filtered between and Hz. Figures (d), (e) and (f) describe the noisy versions of the same normal, inter-ictal and ictal EEG recordings shown in Fig. (a), (b) and (c), respectively. The amplitudes of the eye-blinking and muscle artifacts are adjusted to produce noisy signals with a db signal-to-noiseratio (SNR). Matlab TM software was used to simulate the artifacts and append them to the clean EEG data. B. EEG Spectrum In this study, we apply the proposed feature learning method to the EEG frequency spectrum. Only the distinctive frequency components are nominated as delegate features for the classification of an epileptic seizure. Figure shows the frequency spectrum of clean and noisy EEG signals. The noisy signals were corrupted with simulated eye-blinking and muscle artifacts, and its SNR was db. Figures (a), (b) and (c) depict the frequency spectrum of clean EEG signals for normal, inter-ictal and ictal activities, respectively. Figure (d), (e) and (f) display the frequency spectrum of the noisy EEG signals for normal, inter-ictal and ictal activities, respectively. The figure clearly shows how the eye-blinking and muscle artifacts noticeably change the waveform shape of the EEG spectrum. III. METHODOLOGY This section demonstrates how the LASSO regression model was applied to the EEG spectrum so that only few features were selected. It also demonstrates how the extracted features were then used to train and test the RF classifier. A. Feature Learning: LASSO Regression We present a computationally efficient feature learning method for early detection of epileptic seizures. First, the spectrum of the captured EEG signals are simultaneously obtained by performing a fast Fourier transform (FFT). LASSO regression is then adopted as a feature extraction method applied to the EEG spectrum [5]. Finally, the randomized coordinate descent (RCD) algorithm is used to solve LASSO and nominate the EEG spectral features pertinent to seizures [], [7]. In this work, we address the classification problem between the three EEG sets of Normal, Inter-ictal and Ictal. Each set includes EEG signals, each of,9 time-samples [3]. Accordingly, the total number of EEG signals, defined as n, is 3. The FFT is deployed to attain the spectrum of all EEG signals. Default FFT settings were used (i.e., the FFT size was set to the length of the signal [,9] rounded up to the 95

3 next power of ). In our case, the EEG spectrum is found to include,9 frequency components. Then, we defined a matrix A (n d) that includes the spectrum of the n EEG signals [a ; a ;... ; a n ]. The number of columns in A, defined as d, denote the number of frequency components in each a i. We also defined the column vector b as an n-length vector including the class labels b i s that correspond to a i s in A. The labels of, and 3 were given to the EEG classes of normal, inter-ictal and ictal, respectively. The mathematical description of A and b is shown as: a a a a 3... a d b a A =. = a a a 3... a d & b = b. a n a n a d a d3... a nd b n The feature learning problem can then be formalized as finding the sparse vector β that yields the minimum leastsquare error Aβ b. The solution can be found by solving the following LASSO regression problem [5]: β = arg min Aβ b + λ β () β R d where λ is the LASSO tuning parameter that controls the sparsity level in β. A wealth of methods that can solve LASSO problems have been reported in the literature []. Amongst these methods, the RCD algorithm is found to converge substantially faster than the others [7]. RCD is favourable for minimizing convex functions that take the form: arg min β R d g (β) + d ( ) h j βj j= where g is a smooth function, h is a separable function, and d is the total number of regression coefficients. Interestingly, the LASSO problem shown in () can be rephrased to take the same expression as (): arg min β R d () n ) d (β T a i b i + λ β j (3) i= j= Thereafter, the RCD algorithm is applied to (3) to find the sparse vector β. The full algorithm description is given in Algorithm. After obtaining the sparse vector β using Algorithm, the EEG frequency components that correspond to the nonzero elements of β are selected as delegate features for EEG classification. B. Feature Classification: Random Forest In this work, we examined the performance of several classification models and found that the RF classifier achieves the superior performance in terms the of classification accuracy and computational time. The holdout method was used to Algorithm : Randomized Coordinate Descent [7]. Input: A, b Output: Sparse vector β 3 Initialization: β ; k ; λ = e 7 ; Initialization: Lipschitz constant: L = A ; 5 repeat Pick a random coordinate j k from {,,..., d}; 7 Set α k L(j k ); ] 8 Set β k+ β k α k [ f(β k ) e jk ; j k 9 k k + ; Until Aβ b ɛ analyze the classification performance, where the dataset was divided into two sets, 9% for training and % for testing. The RF classifier integrates a set of independent decision tree classifiers [8]. A decision tree with M leaves splits the feature space into M regions R m, m M. For each tree, the prediction function f(x) is defined as: f(x) = M c m (x, R m ) () m= where M is the number of regions in the feature space, R m is a region corresponding to m, c m is a constants corresponding to m, and is the indicator function. {, if x R m (x, R m ) = (5), otherwise The final classification decision is made from the majority vote of all trees. In this study, we used an RF classifier with trees. IV. RESULTS AND DISCUSSION To evaluate the performance of our feature learning method, we compare our seizure detector with the state-of-the-art detectors that use the same benchmark dataset [] []. The classification performance was measured using the standard metrics of sensitivity, specificity, classification accuracy, and latency (detection delay) [9]. A. Seizure Detection in Ideal Conditions The proposed method is first examined in the ideal conditions, where the EEG recordings are assumed to be free of noise. The spectrum of the clean EEG signals is first obtained and fed into the LASSO regression-based feature learning model with the specific goal of efficient feature extraction. The RCD algorithm is used to solve the LASSO problem and extract the most robust EEG spectral components in a time-efficient manner. The forepart of Fig. 3 displays the spectrum of a clean EEG signal including all,9 frequency components, while the rear part of Fig. 3 shows the extracted frequency components using LASSO. It is worth highlighting that only components out of,9 are nominated as delegate features for EEG classification. 955

4 Selected Frequency Components 5 5 Selected Frequency Components Magnitude (µ V) 3 Original Frequency Components Magnitude (µ V) 3 Original Frequency Components Fig. 3. Original and extracted frequency components of clean EEG spectrum. Fig.. Original and extracted frequency components of noisy EEG spectrum. TABLE I SEIZURE DETECTION RESULTS OF THE PROPOSED AND STATE-OF-THE-ART METHODS; NR = NOT REPORTED. Methods Year Classifier Sensitivity Specificity Accuracy Latency (%) (%) (%) (Sec) Ubeyli et al. [] 9 MLPNN NR Song et al. [5] ELM NR Naghsh et al. [] MLPNN NR Liu et al. [7] SVM NR Chiang et al. [8] SVM NR Chaurasiya et al. [9] 5 SVM NR Hosseini et al. [] SVM NR Behara et al. [] LSSVM Vidyaratne et al. [] 7 RVM Proposed Method 7 RF....8 More interestingly, we compare the performance of our seizure detection model with those of relevant methods in literature that also use the same benchmark dataset [] []. The performance metrics of the proposed and reference seizure detection methods are summarized in Table I. The proposed model achieves a seizure sensitivity of %, which is considerably higher than those of the state-of-the-art methods reported in Table I. It also produces a notable seizure specificity of %, which is comparable to Vidyaratne et al. s results [], and is superior to those of the reference methods. Further, amongst all the existing seizure detection methods, the proposed scheme yields the highest classification accuracy of %. Meanwhile, the short detection latency of.8 seconds proves the time-efficient capability of the proposed model for epileptic seizure onset detection. B. Seizure Detection in Noisy Environments We further examine the effectiveness of the proposed seizure detection model for classifying noisy EEG data. In our previous work, we relaxed introduced a reliable EEG feature learning method capable of performing on noisy signals []. This method, however, assumed that the noise encountered with EEG data acquisition has a Gaussian distribution, which is not the case in practical situations. In this work, we introduce a more practical feature learning method that can address noisy EEG data corrupted with real physical noise (e.g., eye-blinking and muscle artifacts). Several interesting observations can be made here. First, the proposed method can effectively learn the EEG spectral features associated with seizure, even when the EEG data are immersed in noise. Figure shows how the proposed method can adaptively select a different set of robust features from the noisy EEG spectrum. The frontal part of Fig. shows the frequency spectrum of a noisy EEG signal contaminated with eye-blinking and muscle artifacts such that its SNR is db. The rear part of Fig. displays the selected frequency components from the spectrum of the noise-corrupted data. Moreover, it is observed that the proposed method can maintain high seizure detection accuracy in noisy environments. It achieves sensitivity, specificity, and classification accuracy of 9.95%, 99.3%, and 98.7% respectively. V. CONCLUSION For early detection of epileptic seizures, a computationally efficient model that adaptively learns robust EEG spectral features is proposed. The feature learning problem is formalized as a LASSO regression model and solved by the fast coordinate descent method. These features are used with an RF classifier to recognize the epileptic EEG activities associated with seizures. The results demonstrate that our model surpasses the state-of-the-art seizure detection methods, achieving % classification accuracy. The proposed model is also proven to be robust against some artifacts. When the data is corrupted by eye-blinking and muscle artifacts with a SNR of db, the classification accuracy attained was 98.7%. Our model, however, is not robust against other EEG artifacts (e.g., power line interference, EKG and motion artifacts). Future work will address these types of noise. ACKNOWLEDGEMENT This work was made possible by NPRP grant from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. 95

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