Prediction of seizures in sleep based on power spectrum.
10.7507/1001-5515.201708062
- Author:
Weinan LIU
1
,
2
;
Yan LIU
1
,
3
;
Baotong TONG
4
;
Lingxiao ZHAO
4
;
Yingxue YANG
5
,
6
;
Yuping WANG
5
,
6
;
Yakang DAI
7
Author Information
1. Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences, Suzhou, jiangsu 215163, P.R.China
2. University of Chinese Academy of Sciences, Beijing 100049, P.R.China.
3. Harbin University of Science and Technology, Harbin 150080, P.R.China.
4. Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences, Suzhou, jiangsu 215163, P.R.China.
5. Xuanwu Hospital Capital Medical University, Beijing 100053, P.R.China
6. Beijing Key Laboratory of Neuromodulatio, Beijing 100053, P.R.China.
7. Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences, Suzhou, jiangsu 215163, P.R.China.daiyk@sibet.ac.cn.
- Publication Type:Journal Article
- Keywords:
electroencephalogram signals;
power spectrum;
seizure prediction;
support vector machine
- From:
Journal of Biomedical Engineering
2018;35(3):329-336
- CountryChina
- Language:Chinese
-
Abstract:
Seizures during sleep increase the probability of complication and sudden death. Effective prediction of seizures in sleep allows doctors and patients to take timely treatments to reduce the aforementioned probability. Most of the existing methods make use of electroencephalogram (EEG) to predict seizures, which are not specific developed for the sleep. However, EEG during sleep has its characteristics compared with EEG during other states. Therefore, in order to improve the sensitivity and reduce the false alarm rate, this paper utilized the characteristics of EEG to predict seizures during sleep. We firstly constructed the feature vector including the absolute power spectrum, the relative power spectrum and the power spectrum ratio in different frequencies. Secondly, the separation criterion and branch-and-bound method were applied to select features. Finally, support vector machine classifier were trained, which is then employed for online prediction. Compared with the existing method that do not consider the characteristics of sleeping EEG (sensitivity 91.67%, false alarm rate 9.19%), the proposed method was superior in terms of sensitivity (100%) and false alarm rate (2.11%). This method can improve the existing epilepsy prediction methods and has important clinical value.
