Research progress of epileptic seizure predictions based on electroencephalogram signals.
10.7507/1001-5515.202105052
- Author:
Changming HAN
1
;
Fulai PENG
2
;
Cai CHEN
2
;
Wenchao LI
2
;
Xikun ZHANG
2
;
Xingwei WANG
2
;
Weidong ZHOU
1
Author Information
1. School of Microelectronics, Shandong University, Jinan 250101, P.R.China.
2. Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan 250000, P.R.China.
- Publication Type:Review
- Keywords:
deep learning;
electroencephalogram signals;
epilepsy;
machine learning;
seizure prediction
- MeSH:
Electroencephalography;
Epilepsy/diagnosis*;
Humans;
Machine Learning;
Seizures/diagnosis*;
Signal Processing, Computer-Assisted
- From:
Journal of Biomedical Engineering
2021;38(6):1193-1202
- CountryChina
- Language:Chinese
-
Abstract:
As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.
