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.Comment: 8 pages, 6 figures, accepted for publication at the 13th Asia Pacific
Signal and Information Processing Association Annual Summit and Conference
(APSIPA ASC