Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

Abhijit Bhattacharyya,Ram Pachori,U Acharya,Abhay Upadhyay

doi:10.3390/app7040385

Abstract

This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high- and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.

Highlights

Worldwide, around 60 million people are affected by epilepsy disorder, indicating frequent occurrence of seizure events due to hyper-synchronous firing of neuron clusters [1]
Epileptic seizure EEG signal classification has been performed by extracting area features from the second order difference plot and analytic signal representation of intrinsic mode functions (IMFs) extracted from EEG signals using empirical mode decomposition (EMD) [4]
We have presented a new method for the classification of seizure, seizure-free and normal classes of EEG signals

Summary

Introduction

Around 60 million people are affected by epilepsy disorder, indicating frequent occurrence of seizure events due to hyper-synchronous firing of neuron clusters [1]. Classification of epileptic seizure EEG signals has been performed by exploiting the underlying non-stationarity and nonlinearity of EEG signals in previous studies. In [2], the authors used the area of the phase space representations of the intrinsic mode functions (IMFs) as features and achieved a classification accuracy of 98.67% in classifying seizure-free and seizure EEG signals. In [3], bandwidth contributions due to amplitude and frequency modulations of IMFs have been considered as features for the classification of non-seizure and seizure EEG signals and achieved classification accuracy of 99.5–100%. Epileptic seizure EEG signal classification has been performed by extracting area features from the second order difference plot and analytic signal representation of IMFs extracted from EEG signals using empirical mode decomposition (EMD) [4]. Non-stationary signal decomposition techniques and time-frequency domain analysis for the classification of epileptic EEG signals have been studied extensively. In [5], the features extracted from time-frequency distributions, namely Wigner–Ville distribution and short time Fourier transform (STFT), are used for the classification of epileptic seizure

Methods

Results

Discussion

Conclusion