Classification of Epileptic EEG Signals by Wavelet based CFC

TL;DR

This paper introduces a novel wavelet-based cross frequency coupling method for classifying epileptic EEG signals, aiming to improve seizure detection accuracy in computer-aided diagnosis systems.

Contribution

The paper proposes a new wavelet-based CFC feature extraction approach combined with t-test and QDA for improved EEG seizure classification.

Findings

Effective feature extraction using wavelet-based CFC

Optimal features selected by t-test and QDA

Enhanced classification accuracy for epileptic seizures

Abstract

Electroencephalogram, an influential equipment for analyzing humans activities and recognition of seizure attacks can play a crucial role in designing accurate systems which can distinguish ictal seizures from regular brain alertness, since it is the first step towards accomplishing a high accuracy computer aided diagnosis system (CAD). In this article a novel approach for classification of ictal signals with wavelet based cross frequency coupling (CFC) is suggested. After extracting features by wavelet based CFC, optimal features have been selected by t-test and quadratic discriminant analysis (QDA) have completed the Classification.