Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithm

TL;DR

This paper introduces a novel channel selection algorithm and wavelet denoising technique for EEG-based seizure classification, achieving improved accuracy in noisy real-world data.

Contribution

A new channel selection method combined with wavelet denoising enhances EEG seizure detection accuracy in noisy environments.

Findings

Achieved 82% test accuracy on TUH EEG dataset.

Effective noise reduction using Daubechies 4 wavelet denoising.

Improved seizure classification with statistical features and bagged trees.

Abstract

Epilepsy is a disorder of the nervous system that can affect people of any age group. With roughly 50 million people worldwide diagnosed with the disorder, it is one of the most common neurological disorders. The EEG is an indispensable tool for diagnosis of epileptic seizures in an ideal case, as brain waves from an epileptic person will present distinct abnormalities. However, in real world situations there will often be biological and electrical noise interference, as well as the issue of a multichannel signal, which introduce a great challenge for seizure detection. For this study, the Temple University Hospital (TUH) EEG Seizure Corpus dataset was used. This paper proposes a novel channel selection method which isolates different frequency ranges within five channels. This is based upon the frequencies at which normal brain waveforms exhibit. A one second window was selected, with a 0.5 second overlap. Wavelet signal denoising was performed using Daubechies 4 wavelet decomposition, thresholding was applied using minimax soft thresholding criteria. Filter banking was used to localise frequency ranges from five specific channels. Statistical features were then derived from the outputs. After performing bagged tree classification using 500 learners, a test accuracy of 0.82 was achieved.