Intelligence-aided diagnosis of Parkinson's disease with rapid eye movement sleep behavior disorder based on few-channel electroencephalogram and time-frequency deep network / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 1043-1053, 2021.
Artigo
em Chinês
| WPRIM
| ID: wpr-921844
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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Doença de Parkinson
/
Polissonografia
/
Transtorno do Comportamento do Sono REM
/
Eletroencefalografia
/
Inteligência
Tipo de estudo:
Estudo diagnóstico
/
Estudo prognóstico
Limite:
Humanos
Idioma:
Chinês
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2021
Tipo de documento:
Artigo
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