Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks

Abstract

Epilepsy is the most prevalent neurological disorder in humans after stroke. Recurrent seizure is the main characteristic of the epilepsy. Electroencephalogram (EEG) is the recording of brain electrical activity and it contains valuable information related to the different physiological states of the brain. Thus, EEG is considered an indispensable tool for diagnosing epilepsy in clinic applications. Since epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recordings is highly required. Multiwavelets, which contain several scaling and wavelet functions, offer orthogonality, symmetry and short support simultaneously, which is not possible for scalar wavelet. With these properties, recently multiwavelets have become promising in signal processing applications. Approximate entropy is a measure that quantifies the complexity or irregularity of the signal. This paper presents a novel method for automatic epileptic seizure detection, which uses approximate entropy features derived from multiwavelet transform and combines with an artificial neural network to classify the EEG signals regarding the existence or absence of seizure. To the best knowledge of the authors, there exists no similar work in the literature. A well-known public dataset was used to evaluate the proposed method. The high accuracy obtained for two different classification problems verified the success of the method.