Epilepsy detection and analysis method for specific patient based on data augmentation and deep learning.
10.7507/1001-5515.202107060
- Author:
Yong YANG
1
;
Xiaolin QIN
1
;
Xiaoguang LIN
2
;
Han WEN
1
;
Yuncong PENG
1
Author Information
1. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu 610041, P. R. China.
2. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, P. R. China.
- Publication Type:Journal Article
- Keywords:
Deep learning;
Electroencephalogram signal;
Ensemble approach;
Epileptic seizure;
Transfer learning
- MeSH:
Algorithms;
Child;
Deep Learning;
Electroencephalography;
Epilepsy/diagnosis*;
Humans;
Seizures/diagnosis*;
Signal Processing, Computer-Assisted;
Wavelet Analysis
- From:
Journal of Biomedical Engineering
2022;39(2):293-300
- CountryChina
- Language:Chinese
-
Abstract:
In recent years, epileptic seizure detection based on electroencephalogram (EEG) has attracted the widespread attention of the academic. However, it is difficult to collect data from epileptic seizure, and it is easy to cause over fitting phenomenon under the condition of few training data. In order to solve this problem, this paper took the CHB-MIT epilepsy EEG dataset from Boston Children's Hospital as the research object, and applied wavelet transform for data augmentation by setting different wavelet transform scale factors. In addition, by combining deep learning, ensemble learning, transfer learning and other methods, an epilepsy detection method with high accuracy for specific epilepsy patients was proposed under the condition of insufficient learning samples. In test, the wavelet transform scale factors 2, 4 and 8 were set for experimental comparison and verification. When the wavelet scale factor was 8, the average accuracy, average sensitivity and average specificity was 95.47%, 93.89% and 96.48%, respectively. Through comparative experiments with recent relevant literatures, the advantages of the proposed method were verified. Our results might provide reference for the clinical application of epilepsy detection.
