Automatic epileptic seizure detection algorithm based on dual density dual tree complex wavelet transform.
10.7507/1001-5515.202105075
- Author:
Tongzhou KANG
1
;
Rundong ZUO
1
;
Lanfeng ZHONG
1
;
Wenjing CHEN
2
;
Heng ZHANG
2
;
Hongxiu LIU
3
;
Dakun LAI
1
Author Information
1. School of Electronic Science and Engineering, University of Electronic Science and Technology, Chengdu 610054, P.R.China.
2. West China Hospital, Sichuan University, Chengdu 610041, P.R.China.
3. 29th Research Institute of CETC, Chengdu 610093, P.R.China.
- Publication Type:Journal Article
- Keywords:
dual density dual tree complex wavelet;
epilepsy;
epileptic seizure detection;
intracranial electroencephalogram;
wavelet entropy
- MeSH:
Algorithms;
Electroencephalography;
Epilepsy/diagnosis*;
Humans;
Seizures/diagnosis*;
Signal Processing, Computer-Assisted;
Support Vector Machine;
Wavelet Analysis
- From:
Journal of Biomedical Engineering
2021;38(6):1035-1042
- CountryChina
- Language:Chinese
-
Abstract:
It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experimental results showed that the features selected in this paper were different between seizure and non-seizure. Among the eight patients, the average accuracy of three-level decomposition classification was 91.98%, the sensitivity was 90.15%, and the specificity was 93.81%. The work of this paper shows that our algorithm has excellent performance in the two classification of EEG signals of epileptic patients, and can detect the seizure period automatically and efficiently.
