Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier

TL;DR

This paper presents a wavelet-neural network classifier that effectively recognizes and classifies EEG signals from healthy and epileptic subjects by analyzing energy distribution features at multiple resolution levels.

Contribution

The study introduces a novel wavelet-based neural network approach that utilizes energy distribution features for EEG signal classification, demonstrating high efficiency.

Findings

High classification accuracy for EEG signals

Effective differentiation between healthy and epileptic EEGs

Robust performance across diverse EEG datasets

Abstract

In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (delta, theta, alpha, beta and gamma) and the Parsevals theorem are employed to extract the percentage distribution of energy features of the EEG signal at different resolution levels. Second, the neural network (NN) classifies these extracted features to identify the EEGs type according to the percentage distribution of energy features. The performance of the proposed algorithm has been evaluated using in total 300 EEG signals. The results showed that the proposed classifier has the ability of recognizing and classifying EEG signals efficiently.