Advanced Signal Processing Techniqes to Study Normal and Epileptic EEG

TL;DR

This paper employs Fourier and Wavelet transforms to analyze normal and epileptic EEG signals, extracting features across frequency bands for seizure detection using ANN classification.

Contribution

It introduces a combined approach of signal processing and neural networks for improved EEG-based epilepsy diagnosis.

Findings

Effective extraction of EEG features across multiple frequency bands.

Successful classification of epileptic and normal EEG signals using ANN.

Demonstrated potential for automated seizure detection.

Abstract

EEG monitoring has an important milestone provide valuable information of those candidates who suffer from epilepsy.In this paper human normal and epileptic Electroencephalogram signals are analyzed with popular and efficient signal processing techniques like Fourier and Wavelet transform. The delta, theta, alpha, beta and gamma sub bands of EEG are obtained and studied for detection of seizure and epilepsy. The extracted feature is then applied to ANN for classification of the EEG signals.