Seizure classification of EEG based on wavelet signal denoising using a novel channel selection algorithm

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 electroencephalogram (EEG) is an indispensable tool for diagnosis of epileptic seizures in an ideal case, as brain waves from an epileptic patient would present distinct abnormalities. However, in real world situations there will often be biological and electrical noise interference, as well as the issue of a multi-channel signal, which introduce a great challenge for seizure detection and classification. 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 s 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 trees classification using 500 learners, a test accuracy of 0.82 was achieved.