Intelligence-aided diagnosis of Parkinson's disease with rapid eye movement sleep behavior disorder based on few-channel electroencephalogram and time-frequency deep network.
10.7507/1001-5515.202009067
- Author:
Weifeng ZHONG
1
;
Zhi LI
1
;
Yan LIU
2
;
Chenchen CHENG
2
;
Yue WANG
3
;
Li ZHANG
4
;
Shulan XU
4
;
Xu JIANG
4
;
Jun ZHU
4
;
Yakang DAI
2
Author Information
1. School of Automation, Harbin University of Science and Technology, Harbin 150080, P.R.China.
2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P.R.China.
3. Jinan Guoke Medical Engineering Technology Development Co., LTD, Jinan 250102, P.R.China.
4. Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing 210029, P.R.China.
- Publication Type:Journal Article
- Keywords:
Parkinson’s disease;
few-channel scalp electroencephalogram;
intelligence-aided diagnosis;
rapid eye movement sleep behavior disorder;
time-frequency deep network
- MeSH:
Electroencephalography;
Humans;
Intelligence;
Parkinson Disease/diagnosis*;
Polysomnography;
REM Sleep Behavior Disorder/diagnosis*
- From:
Journal of Biomedical Engineering
2021;38(6):1043-1053
- CountryChina
- Language:Chinese
-
Abstract:
Aiming at the limitations of clinical diagnosis of Parkinson's disease (PD) with rapid eye movement sleep behavior disorder (RBD), in order to improve the accuracy of diagnosis, an intelligent-aided diagnosis method based on few-channel electroencephalogram (EEG) and time-frequency deep network is proposed for PD with RBD. Firstly, in order to improve the speed of the operation and robustness of the algorithm, the 6-channel scalp EEG of each subject were segmented with the same time-window. Secondly, the model of time-frequency deep network was constructed and trained with time-window EEG data to obtain the segmentation-based classification result. Finally, the output of time-frequency deep network was postprocessed to obtain the subject-based diagnosis result. Polysomnography (PSG) of 60 patients, including 30 idiopathic PD and 30 PD with RBD, were collected by Nanjing Brain Hospital Affiliated to Nanjing Medical University and the doctor's detection results of PSG were taken as the gold standard in our study. The accuracy of the segmentation-based classification was 0.902 4 in the validation set. The accuracy of the subject-based classification was 0.933 3 in the test set. Compared with the RBD screening questionnaire (RBDSQ), the novel approach has clinical application value.
