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. 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.
:max_bytes(150000):strip_icc()/epilepsy-overview-4155857_final_edited-dcaf959b3f214bc3b6b6e48302a5430a.png "eeg normal vs epilepsy")
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.
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