Detection of epileptic seizure based on entropy analysis of short-term EEG.

ABSTRACT: Entropy measures that assess signals' complexity have drawn increasing attention recently in biomedical field, as they have shown the ability of capturing unique features that are intrinsic and physiologically meaningful. In this study, we applied entropy analysis to electroencephalogram (EEG) data to examine its performance in epilepsy detection based on short-term EEG, aiming at establishing a short-term analysis protocol with optimal seizure detection performance. Two classification problems were considered, i.e., 1) classifying interictal and ictal EEGs (epileptic group) from normal EEGs; and 2) classifying ictal from interictal EEGs. For each problem, we explored two protocols to analyze the entropy of EEG: i) using a single analytical window with different window lengths, and ii) usi

SUBMITTER: Li P 

PROVIDER: S-EPMC5854404 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature