Itaf Ben Slimen, Larbi Boubchir, Zouhair Mbarki, Hassene Seddik

1Centre de Recherche et de Production Research Lab., Ecole Nationale Supérieure des Ingénieurs de Tunis, University of Tunis, Tunis 1008, Tunisia;2Laboratoire d'Informatique Avancée de Saint-Denis Research Lab., University of Paris 8, Saint-Denis, Cedex 93526,France.

Abstract The visual analysis of common neurological disorders such as epileptic seizures in electroencephalography(EEG) is an oversensitive operation and prone to errors, which has motivated the researchers to develop effective automated seizure detection methods. This paper proposes a robust automatic seizure detection method that can establish a veritable diagnosis of these diseases. The proposed method consists of three steps: (i) remove artifact from EEG data using Savitzky-Golay filter and multi-scale principal component analysis (MSPCA), (ii) extract features from EEG signals using signal decomposition representations based on empirical mode decomposition(EMD), discrete wavelet transform (DWT), and dual-tree complex wavelet transform (DTCWT) allowing to overcome the non-linearity and non-stationary of EEG signals, and (iii) allocate the feature vector to the relevant class (i.e., seizure class "ictal" or free seizure class "interictal") using machine learning techniques such as support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA). The experimental results were based on two EEG datasets generated from the CHB-MIT database with and without overlapping process. The results obtained have shown the effectiveness of the proposed method that allows achieving a higher classification accuracy rate up to 100% and also outperforms similar state-of-the-art methods.

Keywords: electroencephalography, epileptic seizure detection, feature extraction, dual-tree complex wavelet transform, machine learning

Introduction

According to the World Health Organization,epilepsy affects about 1% to 2% of the world's population; about 65 million people have epilepsy[1–2].Different examinations are used to confirm the epilepsy diagnosis, such as electroencephalogram(EEG) signals recorded from the patient's scalp, which is a collection of potential signals from many electrodes and they represent the spatial distribution of the potential fields in the brain. EEG is an invaluable tool for characterization of the spatial-temporal dynamics of neuronal activity in the brain; hence,diagnosis by manual observation of these signals is a very sensitive task that resulted in practical tools for the research area dedicated to the diagnosis of seizure detection[3].

More than 120 million articles cited for epileptic seizure detection proved that this subject is still topical and should be addressed. Various epileptic seizure detection methods have been proposed in literature,and a brief review of some techniques related to our work was conducted and introduced in the next section. Gotman and Gloor were among the first to detect seizures using EEG signals[4]. The first machine learning approach was developed using the CHB-MIT database[5]. Some researchers have used a large database, extracted from Freiburg and CHB-MIT databases for seizure detection, and their proposed method allows achieving a high detection performance up to 100%[6]. Some proposed methods are based on artificial neural networks allowing achieving a perfect classification rate for detecting epileptic seizures[7].Some are based on support vector machine (SVM)with time-frequency features and wavelet variance features allowing achieving correct classification rates up to 100%[8]. Another seizure detection method is based on dual-tree complex wavelet transformation(DTCWT) and Fourier features allowing achieving a correct classification rate up to 100%[9].

Various approaches for epileptic seizure detection have been proposed in literature. The processing of EEG signals requires that these data must be noiseless to establish reliable disease diagnosis; therefore, a filter step is necessary for this purpose. Some researchers proposed to use the Savitzky-Golay filter and have its performance verified[10], while others proposed a multi-scale principal component analysis(MSPCA)[11]. In general, all had adopted the objectives of the extraction characteristics and their classification, in order to differentiate between classes(i.e., seizure free: "interictal" class and seizure: "ictal"class). Frequency domain analysis[11]and other timefrequency approaches, such as the short-term Fourier transform[12]and the wavelets[13,14–15]have often been used to extract discriminant characteristics representing the EEG signals. Advanced detection of epileptic seizures used a feature extraction technique based on wavelets[16]. Some features have shown high performance for characterization changes in EEG signals such as entropy, energy[17–18], principal component analysis (PCA)[19], and independent component analysis (ICA).

Based on these previous studies, the discrete wavelet transform (DWT) is one of the most widely used techniques for extracting EEG signal characteristics. For the same purpose, the DTCWT technique[20], applies filter banks to handle the wavelet domain. This technique was recently applied for epileptic seizure detection which demonstrated its performance for extracting the characteristics of the time-frequency domain of EEG signals[21]. After extracting features, the next step is to employ classification methods in order to determine the class of input features. Various classification methods are available. These range from rule-based decisionmaking[4]and SVM[22]to artificial neural network(ANN). For example, the classification of EEG signals was investigated using k-means classifier and multilayer perceptron neural network model[14]. A supervised automatic learning method was proposed for detecting seizures using linear discriminant analysis (LDA)[23].

The goal of this work is to build a reliable and accurate seizure detection method to facilitate and accomplish the diagnosis of epilepsy. Artifact eliminator is a valuable aid for diagnosis in EEG signal, using the Savitzky-Golay filter and the MSPCA. To overcome the non-linearity and nonstationary of EEG signals, the time-frequency techniques have been developed to eliminate the strict restrictions of classical frequency methods. Three popular signal decomposition techniques chosen in this work include empirical mode decomposition(EMD), DWT, and DTCWT. After crossing through a step of denoising the artifacts and extracting the frequency bands using EMD, DWT, and DTCWT,statistical values were used to extract the relevant characteristics of these frequency bands. Then three classifiersk-nearest neighbor (k-NN, LDA, and SVM were established to distinguish between classes (i.e.,interictal and ictal).

The choice of MSPCA and Savitzky-Golay for artifact removal is justified by their proven superiority in different biomedical signals kinds, such as ECG[24],EMG[25], and EEG[26]. The plethora of EMD, DWT,and DTWCT signal decomposition methods used in different fields of analysis justifies the choice in this work. This step is followed by the extraction of the statistical characteristics to extract the necessary information with reduction of used data dimensions.The aforementioned modulators were combined with three popular machine-learning techniques to find a powerful model for detecting epileptic seizures.Therefore, this work aims to develop an EEG signal classification system for seizure detection using CHBMIT (EEG scalp) databases, and to ensure the robustness of the proposed model in which two databases have been extracted and realized based on EEG segments with and without overlapping procedure. The results proved that the proposed model is suitable for epileptic seizure detection.Fig. 1illustrates the work plan presented in this study.

Materials and methods

The lack of visual difference between the free seizure and seizure EEG signals makes the epileptic seizure detection a difficult task. Therefore, three proposed steps were employed, with EEG signal denoising techniques followed by features extraction methods to enable faster and more effective seizure detection. Hence, the classification using the extracted characteristics is performed using different machine learning. The EEG database used in our study is presented with a brief description.

EEG database

All tests and simulations were conducted based on the database collected at the Boston Children's Hospital (CHB) and used to validate the experimental results. This "CHB-MIT" database includes 23 different subsets containing EEG records of pediatric patients. It contains 182 seizures. All signals were sampled at 256 Hz per second with a resolution of 16 bits. Most files contain 23 EEG signals. The international 10-20 EEG electrodeposition and nomenclature system was used for these recordings[5].

EEG signal pre-processing

Biomedical signals such as EEG were contaminated by artifacts and noise, and thus a pre-processing step is needed to clean the data. Many signal-filtering algorithms have been proposed to improve the performance of health devices (EEG, ECG, EMG...)besieged by artifacts. Savitzky-Golay filter and multiscale principal component analysis have been used in recent research and their performance has been verified.

Savitzky-Golay filter

During the pre-processing of biomedical signals,the most challenging problem is to extract high resolution EEG signals from noisy measurements and retain the EEG waveform sharpness. The spectral quality of a signal [signal-to-noise (S/N) ratio]may be improved by increasing filter width or smoothing the signal multiple times. Hence, The Savitzky-Golay filter is a low-pass filter used for attenuation noise,without damaging or destroying the spectral properties of data. This filter is defined as a weighted moving average that performs a least-squares-fit convolution procedure.

Therefore, the S–Golay algorithm is characterized by the selected data interval, in other words, the window size, a polynomial function is fitted to the chosen size window and the data point (noisy signal)is calculated using the polynomial coefficients. The set of datan{xj,yj} points, wherej=1,2,…,n;xis an independent variable andyis the observed value, can be represented with convolution coefficients calledcandCiand expressed by:

Fig. 1 Diagram for proposed seizure detection approach. MSPCA: multi-scale principal component analysis; DWT: discrete wavelet transform; EMD: empirical mode decomposition; DTCWT: dual-tree complex wavelet transformation; SVM: support vector machine; k-NN:k-nearest neighbor; LDA: linear discriminant analysis.

Hence, S-Golay filter requires the inputs which include: the input noisy signal (x), the polynomial order (k) and its frame size (f). One of the advantages of this filter is to retain distribution characteristics,which are often flattened with other smoothing methods[10,27].

Multi-scale principal component analysis (MSPCA)

The MSPCA approach is proposed to achieve the objective of eliminating noise without sacrificing a significant amount of fast changes in the signal and to provide a dimension reduction with select components relevant in the different scales. The advantages of this method come from the combination of the PCA,which is responsible for extracting the relationship between several variables, and the wavelets, that divide the stochastic processes of the determinists and approximately decorrelate the auto-correlation between the variables. The MSPCA calculates the PCA of all wavelet coefficients for all wavelet decomposition levels, and then establishes a relationship between the results obtained and the suitable levels[24].Fig. 2illustrates the steps of the MSPCA method and the following algorithm explains it:

1) Perform wavelet decomposition at levelIfor each column of data matrixX.

2) For1≤i≤I, perform the PCA of the detailed matrixGiX, choose an appropriate number of significant principal componentspior suppress the detail.

3) Perform the PCA of the approximation matrixHIXand select a suitable number of principal componentspI+1.

4) The main characteristics of the original matrixXput in a new matrix, by inverting theDWTTwavelet transformation.

5) Finally, execute the PCA of[28].

EEG signal decomposition

To characterize the EEG signal and determine its class, some methods have often been used to extract discriminant characteristics representing EEG signals from frequency analysis and time-frequency approaches. These, such as DWT, DTCWT, and EMD, are recently used in the literature and will be used in this work and represented in this section with their proven superiority to reach this goal.

Empirical mode decomposition (EMD)

EMD is a procedure used to decompose signals into different simple oscillatory modes called intrinsic function mode (IMF). Hence, the signalx(t) can be represented by the sum of these IMFs components and by a residue as follows[29]:

whereMis the number of IMFs andrM(t) is the residue. The IMFs of a signal must satisfy two necessary conditions. Firstly, the difference between the total numbers of extrema and zero-crossings is at most one. Secondly, in all points, the average value of the envelope defined by the local maxima and that by the local minima is zero or very close to zero[30]. The following steps represent the EMD algorithm:

1) Extract local maxima and local minima from signal ofx(t),

2) Connect between all minima and all maxima to obtain two envelopesemin(t) andemax(t),

3) Compute the average between them as:

4) Extract the detail fromx(t) as:d(t)=x(t)-m(t)

5) Examine, ifd(t) is an IMF or not by verifying the above conditions for IMFs,

6) Reiterate on the residuald(t).

After having the first IMFD1(t) of the signal, to get other IMFs, generate the residualr(t) as follows:r(t)=x(t)–D1(t). The process described above will be continued until IMF can no longer be obtained from the final residue.

Discrete wavelet transform (DWT)

Fig. 2 The procedure for MSPCA. DWT: discrete wavelet transform; PCA: principal component analysis.

DWT is one of the most efficient time-frequency tools for non-stationary signal analysis. The DWT procedure makes it possible to present an input signal in sets of functions, called wavelets, by scaling and shifting the mother function into wavelets, that is to say, to decompose a signal into two components called sub-bands: a low frequency component called"approximation coefficients A", and a high frequency component called "detailed coefficients D". The low frequency component can further be decomposed into approximations and details. After each decomposition,the frequency resolution is doubled and the temporal resolution is halved. After choosing the decomposition levelj, the components of the signals(k) can be represented by the following expression andFig. 3[31]:

RegardingFig. 3,handgare low-pass and highpass filters, respectively.sis the input original signal whereCa1,Ca2, andCa3represent the approximation coefficients ofh(1),h(2) andh(3) andCd1,Cd2andCd3represent the detail coefficients ofg(1),g(2) andg(3) at level 1, 2 and 3, respectively.

Dual-tree complex wavelet transform (DTCWT)

DTCWT, a DWT enhancement, uses double wavelet filters low and high at each scale to obtain two parts: a real and an imaginary complex wavelet coefficient. This transformation is invariant by directional and selective shifting in two or more dimensions, which are very important in applications such as pattern recognition and signal analysis.

Hence, the DTCWT can be represented by the following expression[20]:

Fig. 3 DWT decomposition tree.

Fig. 4 An illustrative scheme of the DTCWT at level 3.

where the complex wavelet isψ(t) while the complex scaling function isφ(t). With reference toFig. 4,srepresents the input signal, and the output coefficients of the top tree h and those of the bottom tree g are considered as the real and imaginary parts,respectively. Therefore,and g0are a lowpass filter andh1, andg1are high-pass filters.Consequently, the corresponding waveletsψh(t) andψg(t) form approximately a Hilbert pair, while the scaling functionφg(t) is approximately the Hilbert transform ofφh(t).

Discriminative feature extraction

The classification requires a necessary step whose characteristic extraction aims to obtain the most important information and preserve the small size of the data. Therefore, after signal decomposition using the DWT, EMD and DTCWT methods, the most prominent statistical features have been used for this study to obtain the class of the input EEG signal:

Mean of coefficients in each sub-band:

Average power of coefficients in each sub-band:

Signal classification

Frequency band decomposition is performed to represent an EEG signal in feature vector and to obtain the most important information of the data to determine its class. The identification of signals relied on classification algorithms to estimate the desired class. For this purpose, three popular classifiers are used, namely LDA,k-NN, and SVM, which are briefly explained in this section.

Linear discriminant analysis (LDA)

LDA, also called Fisher's LDA, is a simple

Standard deviation of coefficients in each sub-band:technique used in machine learning, statistics, and pattern recognition to find a linear separating two or more classes, and in general, it gives a good class estimation. Fisher's LDA requires very little computation. LDA aims to use hyperplanes to separate data from different classes, and the separator hyperplane is obtained by looking for the projection that maximizes the distance between classes and minimizes the interclass variance between them. For a class numberN(N>2), several hyperplanes are used[32–33]. In this work, the linear discriminant type was used for regularized linear discriminant analysis.

Support vector machine (SVM)

With excellent performance results, SVM is applied in various fields. Contrary to LDA, which constructs a probabilistic model for each class using all data points, SVM is to minimize a linear separation error by focusing on the neighboring points of the linear separation surface, that is to say, the points located far from the separation surface are ignored in the learning process. SVM is based on the Vapnik-Chervonenkis theory and the principle of structural risk minimization. Its purpose is to determine the minimizing of the training set error by maximizing the boundary of the separating hyper-plane between the data[34–35]. In this work, the Kernel function used to differentiate between the two classes is linear kernel with the Kernel scale parameter chosen to be 1.

k-nearest neighbor (k-NN)

Thek-NN is a machine-learning technique used for the classification task, which aims to find the nearest k-neighbors among the learning set, and the categories ofkclosest neighbors, which are used to weight the candidates in the category. The closest neighbors to the sample are expressed as distance such as Euclidean distance, Chebyshev distance, and Manhattan distance. The performance ofk-NN algorithm strongly depends on two factors: an appropriate similarity function and an appropriate value fork. Ifkis too large, large classes will overwhelm small classes, whereas ifkis too small, the advantage of thek-NN algorithm cannot be traced[36–37,19]. In this research, 3-NN algorithm (3 nearest neighbors) and Euclidean distance metric were used to classify the EEG signals.

Database preparation

The CHB-MIT database is one of the EEG databases used in recent research for epileptic seizure detection. To prove the effectiveness of the proposed method, two EEG datasets have been extracted from the CHB-MIT database. Hence, the extraction of shorter EEG segments based on no overlapping process represents the first used database, while the second based on overlapping process. The segment extraction process of these two datasets is clearly explained in this section. The performance evaluation criteria to assess the performance and the effectiveness of the proposed method and the experimental results obtained will be shown as follows.

The developed seizure detection model is based on EEG segment extraction with an 8-second rectangular window. The seizure time is known and given in the description database for each patient. The first extracted dataset contains 400 EEG segments extracted approximately from all the recorded signals in the CHB-MIT database with 200 no seizure segments ("interictal") and 200 seizure segments("ictal").

According to a previous study[6], the choice of the 8 seconds window size (with 2 048 samples) provides a more robust performance than that of shorter or longer windows. To prove the effectiveness of the proposed seizure detection model, the overlapped database has been created with an overlapping process based on the first extracted database that will be presented as follows.

Overlapping procedure

While the first database was created (with 400 segments), to evaluate the performance and better prove the effectiveness of the proposed seizure detection method, another database will be created based on the first realized with an overlapping process. Hence, new segments will be created by overlapping between two segments belonging to the same EEG signal in the same class. Let suppose that:

-Nis the segment number of the first database already created (without overlapping) calledB.N1andN2is the number of segments in "interictal" classB1and "ictal" classB2respectively,

-Ncis the segment number of the second database with overlapping process calledBc.Nc1andNc2are the numbers of segments in "interictal" classBc1and"ictal" classBc2respectively.

Therefore, the overlap process is presented as follows:

-nrepresents the segment samples number (2 048),iis segment index (ithsegment),pis the point offset for overlap,feis the sampling frequency 256 Hz, andscis the segment seconds number (8 seconds).

Example: let supposeiandi+1are two segments of the same class to be overlapped whereX1i,X2i,X3i,X4i,X5i,X6i,X7i,X8iandX1i+1,X2i+1,X3i+1,X4i+1,X5i+1,X6i+1,X7i+1,X8i+1, are represent the sample of segments ofiandi+ 1, respectively, andjthe new obtained segment with a gap between the segment isp=6.

Finally, the new segment can be presented by the following expression:

The size of new database (overlapped) will be increased compared to the first database.

Therefore, the overlapped database size will be 798 segments, with 399 segments in both classes and each length segment 8 seconds. The experimental results of the proposed approach using the two extracted databases will be presented by following the performance criteria to validate the model accuracy.

Feature selection process

The sampling rates of the databases are 256 Hz; the polynomial order was chosen to be 2 on S-Golay filter and the frame length was selected to be 17, proved to be the parameter' choice[10]. In MSPCA, a five-level decomposition was used to generate the sub-bands signals. The Kaiser rule was responsible for eliminating the number of loadings[26]. After the artifact removal procedure with S-Golay and MSPCA,the features have been extracted from the sub-band decomposed signals using EMD, DWT, and DTCWT methods. The Daubechies wavelets of different orders(2, 3, 4, 5, and 6) are investigated for the analysis of epileptic EEGs. This family of wavelets has orthogonal property and is efficient as a filter implementation[38]. The fourth order Daubechies wavelet is found to be most appropriate for analysis of EEG data[6,39]. Hence, DB4 wavelet function was used to decompose the EEG signals with DWT. The decomposition levels in all methods were selected to be 3. Hence, 4, 8 and 3 sub-band signals were obtained using DWT, DTCWT, and EMD respectively. Therefore, in each sub-band signals three statistical characteristics have been extracted to obtain 12, 24, and 9 features for DWT, DTCWT, and respectively. However, although the number of extracted features was considerably doubled in DTCWT, the performance in the testing step improved.

Performance evaluation criteria

To prove the robustness of the proposed method,cross-validation (CV) is a procedure mainly applied to estimate the proficiency of machine learning algorithm. CV is used to estimate how the model should work when used to make predictions of unused data at the step of preparation and model learning. In a 10 times CV, the database is randomly separated into 10 different mutually exclusive folds of the same size,where nine folds will be used to training the data and the remaining one will be used to test and predict the classification. The CV accuracy is expressed by:

where represents the number of folds used, andAiis the accuracy measure of each,i=1,...,k. In addition,there are three performance measures to evaluate machine learning methods using true positives (TP),false positives (FP), true negatives (TN), and false negatives (FN), which can be expressed by:

Results

The previous sections have shown that the first goal in this work is to find a model to classify EEG signal segments to develop automatic seizure detection systems using the children's EEG database CHB-MIT.However, shorter EEG segments with 8 seconds have been extracted from this database that makes it possible to develop a practical and robust seizure detection system to diagnose epilepsy. Ictal record signals represent a small amount of data compared to interictal record signals. Therefore, a subsampling paradigm of the class with larger proportions(interictal) is used to circumvent the imbalance between the two classes[40], in order to have better accuracy of seizure detection. In this work, segments of interictal data were randomly selected to produce a comparable and balanced number of ictal and interictal segments.

Based on the segment model, the purpose of this study was to develop an EEG signal classification model. Therefore, the contribution is to have a powerful and suitable model using three popular methods to extract discriminant characteristics such as EMD, DWT, and DTCWT, followed by a dimension reduction employing three different statistical characteristics and three commonly used methods of automatic learning such as LDA,k-NN, and SVM.

To prove the performance results of the average precision, extraction methods were evaluated and the results are presented in detail and illustrated inFig. 5.The parameter values of all selected techniques are explained in the preview section because they produce the most robust results. InFig. 5, the plots are shown in the left column present the S-Golay results to apply a series of data points to denoise the signals without deforming its input, while the plots are shown in the right column present the MSPCA results, which shows the advantage of PCA in extracting necessary information and the effectiveness of extracting deterministic features from stochastic processes using wavelets. With the two filter processes compared, it can be found that MSPCA outperformed the S-Golay filter in the removal of artifacts; MSPCA achieved a 100% accuracy with the three classification methods while S-Golay 98%.

With the three EEG signal decomposition methods compared, the results indicate that using the two denoising methods, DTCWT was superior to DWT and much better than EMD where with S-Golay(DTCWT, 99%; DWT, 94%; and EMD, 91%).Fig. 5also shows that the DTCWT gave a fairly high classification result with 100% accuracy classification rates, which is attributed to the approximate shift invariant property, a very important factor in epileptic seizure detection. On aspects of the performance of feature extraction methods, the experimental results show the superiority of the standard deviation and the average power, which both gave a rather high classification accuracy when compared to the mean of the sub-band features. Hence the mean static feature with DTCWT outperforms that with DWT, which ensures the high effectiveness of DTCWT. In addition, the result provides thatk-NN and SVM gave 100% classification accuracy when compared to LDA.

Fig. 5 Classification accuracy of the proposed method using the signal decomposition transforms for feature extraction considered in this study (i.e., EMD, DWT, DTCWT) with EEG signal pre-processing techniques: MSPCA (right column) and S-Golay (left column).

Discussion

The overall results obtained in this study show that combination of the DTCWT with SVM andk-NN improves the accuracy rates of the classification especially by using the standard static deviation and average power characteristics with the two denoising processes. As shown inFig. 5which (MSPCA+DTCWT+STD+SVM), (MSPCA+DTCWT+STD+k-NN, (S-Golay+DTCWT+STD+SVM) and (S-Golay+DTCWT+STD+k-NN achieve a 100% classification accuracy rate. Hence, it can be concluded that the proposed model is reliable and robust for seizure detection with the DTCWT combined with SVM andk-NN.

To compare the results of the proposed approach with seizure detection methods realized in literature,Table 1provides that the proposed approach has achieved an overall perfect accuracy of 100% in seizure detection using EEG segments.

The obtained results using the overlapped database ensure the reliability and stability of this seizure detection model as illustrated inTable 2andFig. 6.The combination of DTCWT and MSPCA offers advantages over the DWT and EMD and keeps the same 100% accuracy performance. The important difference between EMD and wavelet-based methods lies in the process of feature extraction. IMF signals are the product of EMD methods and have the same length as the original EEG signals, which have given no reduction in the number of samples. On the other hand, the coefficients characterizing different subbands produced by wavelet-based methods (DWT and DTCWT) have a much smaller number of samples than the EEG signals input. However, the DWT suffers from certain disadvantages such as the oscillatory nature, the aliasing of the spectrum with limited directional information, and the lack of lag invariance. The DTCWT has solved these problems by its non-oscillating property with approximate invariant shift amplitude where this property is very important for the detection of epileptic seizures in EEG signals.

In conclusion, we have proposed a robust automatic approach for epileptic seizure detection and classification that was assessed on two EEG datasets generated from the CHB-MIT database. The proposed approach is based on two denoising signal methods(i.e., S-Golay and MSPCA) combined with three popular feature extraction techniques (i.e., DTCWT,DWT, and EMD). Three different machine-learning techniques (namely SVM,k-NN, and LDA) have been used to distinguish between the seizure-free and seizure segments based on the feature extraction methods already mentioned. The experimental results have shown that the employment of DTCWT has been very successful because it outperforms the other two methods (DWT and EMD). The results obtained have also clearly shown that MSPCA+DTCWT+SVM andk-NN offers a perfect classification with 100%accuracy. The findings demonstrate the effectiveness of the proposed method compared to existing methods. After completing the seizure detection task,future studies may take more interest in the prediction of epileptic seizures.

Table 1 Comparison of the proposed method against some state-of-the-art methods

Table 2 Classification accuracy of the proposed overlapping method

Fig. 6 The sensitivity and specificity of the proposed method using the signal decomposition transforms for feature extraction considered in this study (i.e., EMD, DWT, DTCWT) with EEG signal pre-processing techniques (MSPCA and S-Golay). MSPCA: multiscale principal component analysis; DWT: discrete wavelet transform; EMD: empirical mode decomposition; DTCWT: dual-tree complex wavelet transformation; SVM: support vector machine; k-NN: k-nearest neighbor; LDA: linear discriminant analysis; Sens: sensitivity; Spec:specificity.