Y. Si
2020.4.29Acta Epileptologica
tlooto Summary
Examination of various ML approaches for electroencephalograph signal procession in epilepsy research highlights applications in the aspect of automated seizure detection, prediction and orientation.
Abstract
Machine learning (ML) is a fundamental concept in the field of state-of-the-art artificial intelligence (AI). Over the past two decades, it has evolved rapidly and been employed wildly in many fields. In medicine the widespread usage of ML has been observed in recent years. The present review examines various ML approaches for electroencephalograph (EEG) signal procession in epilepsy research, highlighting applications in the aspect of automated seizure detection, prediction and orientation. The present review also presents advantage, challenge and future direction of ML techniques in the analysis of EEG signals in epilepsy.
Citation format
SI, Y. Machine learning applications for electroencephalograph signals in epilepsy: A quick review. Acta Epileptologica, 2020, 2: 1–7.