Machine learning applications for electroencephalograph signals in epilepsy: a quick review

• Published in: Acta epileptologica Vol. 2; no. 1; pp. 1 - 7
• Main Author: Si, Yang
• Format: Journal Article
• Language: English
• Published: London BioMed Central 29.04.2020; Nature Publishing Group; BMC
• Subjects: Accuracy; Algorithms; Artificial intelligence; Automation; Classification; Convulsions & seizures; Datasets; Decision trees; Decomposition; Deep learning; Electrodes; Electroencephalograph; Epilepsy; Family Medicine; General Practice; Linear programming; Machine learning; Medicine; Medicine & Public Health; Methods; Neural networks; Neurology; Neuroradiology; Neurosurgery; Review; Seizure; Sensors; Support vector machines; Surgery; Electroencephalograph; Seizure; Epilepsy; Machine learning
• ISSN: 2524-4434; 2096-9384; 2524-4434
• DOI: 10.1186/s42494-020-00014-0

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.