Classification of Focal and Nonfocal EEG Signals using Features Derived from Fourier-based Rhythms Pushpendra Singh1,∗and Ram Bilas Pachori2,∗ 1
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School of Engineering and Applied Sciences, Bennett University, Greater Noida, India Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, India
Abstract We propose a new technique for automated classification of focal and nonfocal electroencephalogram (EEG) signals using Fourier-based rhythms in this paper. The EEG rhythms namely, delta, theta, alpha, beta and gamma, are obtained using the discrete Fourier transform (DFT) based filter-bank applied on EEG signals. The mean-frequency (MF) and root-mean-square (RMS) bandwidth features are derived using DFT based computation on rhythms of EEG signals and their envelopes. These derived features namely, MF and RMS bandwidth have been provided as an input feature set for classification of focal and nonfocal EEG signals using a least-squares support vector machine (LS-SVM) classifier. We present experimental results obtained from the publicly available database in order to demonstrate the effectiveness of the proposed feature sets for automated classification of the the focal and nonfocal classes of EEG signals. The obtained classification accuracy in this dataset for automated classification of focal and nonfocal 50 pairs and 750 pairs of EEG signals are 89.7% and 89.52%, respectively.
Keywords: Focal and nonfocal electroencephalogram (EEG) signals; Mean-frequency and root-mean-square bandwidth features; EEG rhythms; Least-squares support vector machine classifier
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Introduction
A seizure is a short event, characterized by a transient occurrence of signs because of abnormal synchronous neuronal activity in the brain, and epilepsy is a disease of the brain that involves recurrent unprovoked seizures [1]. Epilepsy disease is a neurological disorder, which affects around 1–2 % people worldwide [2]. The epileptogenesis may be the main reason for occurring seizures in the epileptic patients [3, 4]. The electroencephalogram (EEG) signals are generally used by neurologists to understand the electrical activities of the human brain in order to diagnose the disorders as epilepsy [5], autism [6], sleep related disorders [7], alcoholism [8] etc. There may be resistance towards drugs from the epileptic patients who are suffering from generalized and partial epilepsy during their treatment procedure [9]. For the treatment of such patients, identification of the affected area of the brain is required for performing the surgery. The signal processing based techniques can play an important role for identification of the EEG recordings corresponding to the affected area in the brain associated with focal epilepsy [10, 11]. A lot of work has been done for analysis, classification, and detection of epileptic seizures using features extracted from signal processing methods applied on EEG signals [4,12,13]. The wavelet transform (WT) based methods have been investigated for analysis, detection and classification of epileptic seizures using EEG signals [14–16]. The fractional linear prediction based method has been proposed in [17] for classification of seizure and seizure-free EEG signals. Time-frequency localized biorthogonal wavelet filter bank has been explored for detection of epileptic seizure in [18]. The features obtained from the intrinsic mode functions (IMFs) derived from the empirical mode decomposition (EMD) of EEG signals have been explored for analysis, classification, and detection of epileptic seizures [19–23]. The Fourier-based features obtained from the rhythms of EEG signals have been introduced for classification of epileptic seizures [24, 25]. The time-frequency distributions like as smoothed pseudo-Wigner-Ville distribution and short-time Fourier transform have been studied for ∗ Author’s
E-mail address:
[email protected] (P. Singh);
[email protected] (R. B. Pachori).
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detection of epileptic seizures from EEG signals [26, 27]. The tunable-Q wavelet transform (TQWT) and analytic time-frequency flexible wavelet transform have been studied for automated classification of epileptic seizure EEG signals [28, 29]. Recently, studies on focal and nonfocal EEG signals with TQWT, flexible analytic wavelet transform, empirical wavelet transform, and time-frequency localized orthogonal wavelet filter bank methods [30–34] have been performed. The delay permutation index and support vector machine (SVM) have been applied for classification of focal and nonfocal EEG signals [35]. The average sample entropies and average variance of instantaneous frequencies of IMFs together with least-square support vector machine (LS-SVM) have been proposed in [36] for classification of focal EEG signals. In other study, an integrated index based on the discrete wavelet transform (DWT) and entropy measures has been suggested for identification of focal EEG signals [10]. The entropy measures of IMFs have been studied for classification of focal and nonfocal EEG signals [11]. It should be noted that the automatic method based on the signal processing for identification of focal EEG signals can be used for locating the epileptogenic focus. In this paper, we propose a new method for automated classification of focal and nonfocal EEG signals based on the Fourier-based rhythms. The block diagram of the proposed automated classification system for classification of focal and nonfocal EEG signals using Fourier-based rhythms is shown in Figure 1. We obtain the EEG rhythms termed as intrinsic band functions (IBFs) [24] namely gamma, beta, alpha, theta and delta rhythms denoted by γ[n] (> 30 Hz), β[n] (13-30 Hz), α[n] (8-13 Hz), θ[n] (4-8 Hz) and δ[n] (0.1-4 Hz) waves, respectively. In order to capture the slow-varying features of the EEG rhythms, we obtain upper envelope from each of them. In order to remove DC components in estimated parameters from upper envelope, the mean is subtracted from them. The mean-frequency (MF) and root-mean-square (RMS) bandwidth of rhythms and their envelopes have been used as a feature set in this work. In this study, we use a discrete Fourier transform (DFT) based filter bank to obtain rhythms form EEG signals. The DFT is also used to capture mean frequencies and RMS bandwidths from EEG rhythms and mean-subtracted upper envelopes of EEG rhythms. The features set MF, denoted as Rµ and U Eµ , RMS bandwidth (RBW), denoted as Rσ and U Eσ , of EEG rhythms and mean-subtracted upper envelopes of EEG rhythms, respectively are used for EEG signal classification using LS-SVM classifier. The paper is organized as follows: We present the DFT based filtering to obtain EEG rhythms, MF and RBW calculation form EEG rhythms in Section 2. A brief explanation of LS-SVM is presented in Section 3. Section 4 and 5 provide experimental results and discussion, respectively. Section 6 presents conclusions.
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Computation of Fourier-based mean-frequency and RMS bandwidth features
In this paper, we have used Fourier transform (FT) based MF and RMS bandwidth features extracted from the EEG rhythms [24] for classification of focal and nonfocal EEG signals. In order to obtain the EEG rhythms, we apply the DFT based filter bank in proposed method shown in Figure 1. As an example, rhythms obtained from EEG signal are shown in Figure 2. Here, a focal EEG signal (upper x[n]) and nonfocal EEG signal (lower x[n]) are decomposed into seven components namely, noise component, five rhythms and residue component. In this figure, both upper and lower EEG signals x[n] are decomposed such that the sum of the noise component denoted by y7 [n] (>100 Hz), EEG rhythms denoted by y6 [n] (γ[n] rhythm: 30-100 Hz), y5 [n] (β[n] rhythm: 13-30 Hz), y4 [n] (α[n] rhythm: 8-13 Hz), y3 [n] (θ[n] rhythm: 4-8 Hz), y2 [n] (δ[n] rhythm: 0.1-4 Hz) and residue component (y1 [n]
