Abstract
The dynamics of brain area influenced by focal epilepsy can be studied using focal and non-focal electroencephalogram (EEG) signals. This paper presents a new method to detect focal and non-focal EEG signals based on an integrated index, termed the focal and non-focal index (FNFI), developed using discrete wavelet transform (DWT) and entropy features. The DWT decomposes the EEG signals up to six levels, and various entropy measures are computed from approximate and detail coefficients of sub-band signals. The computed entropy measures are average wavelet, permutation, fuzzy and phase entropies. The proposed FNFI developed using permutation, fuzzy and Shannon wavelet entropies is able to clearly discriminate focal and non-focal EEG signals using a single number. Furthermore, these entropy measures are ranked using different techniques, namely the Bhattacharyya space algorithm, Student’s t-test, the Wilcoxon test, the receiver operating characteristic (ROC) and entropy. These ranked features are fed to various classifiers, namely k-nearest neighbour (KNN), probabilistic neural network (PNN), fuzzy classifier and least squares support vector machine (LS-SVM), for automated classification of focal and non-focal EEG signals using the minimum number of features. The identification of the focal EEG signals can be helpful to locate the epileptogenic focus.
Highlights
Epilepsy is characterized by seizures, which may be due to epileptogenesis [1,2]
A new method for the classification of focal EEG signals using an integrated index developed based on entropies is proposed
We have used entropy-based features computed from the discrete wavelet transform (DWT) of EEG signals to perform the classification
Summary
Epilepsy is characterized by seizures, which may be due to epileptogenesis [1,2]. Patients of generalized and partial epilepsy can develop resistance to drugs during the course of epilepsy. The proportion of such patients is approximated to be 20% in generalized epilepsy and 60% in partial epilepsy [3]. In order to treat such patients surgically, the brain area involved in epileptic seizure generation needs to be removed. In this way, the identification of the brain area participating in seizure generation becomes an important step before the surgery
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