Prediction of Epileptic Seizures: A Statistical Approach with DCT Compression

Abstract

Electroencephalogram (EEG) signal compression is an essential process to speed-up the medical signal transmission with reduced storage requirements, costs, and required bandwidth. The main objective of the present work is to compress the EEG signals and study the effect of the compression process on the epileptic seizure prediction. A lossy EEG data compression scheme using Discrete Cosine Transform (DCT) is applied, followed by seizure prediction. The used dataset includes healthy, pre-ictal, and ictal signals with multiple channels. The EEG signals are segmented to segments of 10 sec length. Also, the probability density functions (PDFs) for seizure prediction are measured, including amplitude, derivative, local media, local variance, and local mean PDFs. During the testing phase, only the selected bins of PDFs are used in the prediction process to identify each signal segment as pre-ictal or normal. A method of equal benefit decision fusion is carried out in the final prediction stage leading to a single sequence of decisions representing the activities of all segments. Relative to a patient-specific estimation level, this series after being filtered with a moving average filter is compared.