Seizure Detection Algorithms Based on Analysis of EEG and ECG Signals: a Survey

摘要

Epilepsy is a chronic disorder of the CNS that predisposes individuals to recurrent seizures. Computerized seizure detection algorithms will enable alerting systems that may decrease the harm of the seizures. This paper attempts to provide a comprehensive survey of different types of seizure detection algorithms and their potential role in diagnostic and therapeutic applications. Major recent algorithms use electroencephalogram (EEG) and electrocardiogram (ECG) signals to detect the seizure onset and seizure event. In these algorithms, various features arc extracted from the EEG signal alone or in concert with the ECG signal until the patients are classified into two classes, seizure and non-seizure. We identify three major categories for seizure detectors; EEG-based seizure-event detectors, EEG-based seizure-onset detectors, and EEG/ECG-based seizure-onset detectors. In addition, some other related issues, such as dataset and evaluation measures, are also discussed. Finally, the performance of algorithms is evaluated, and their capabilities and limitations are described.