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.
针对临床帕金森病（PD）伴快速眼动睡眠行为障碍（RBD）诊断方法的局限性，为了提高诊断准确率，提出基于少导联脑电和时频深度网络的智能辅助诊断方法。首先，为提高运算速度及算法鲁棒性，对各被试者的6导联头皮脑电数据进行等长的时间窗提取；然后，基于时间窗脑电数据构建时频深度网络，并得到基于时间窗的分类结果；最后，对各被试者所有时间窗脑电数据的分类结果进行综合决策，实现基于被试者的PD伴RBD辅助诊断。本文以南京医科大学附属脑科医院采集的PD伴和不伴RBD患者的多导睡眠图（PSG）为研究对象，两类数据各30例，以医生诊断结果作为金标准进行算法性能验证。本文方法基于时间窗的分类准确率为0.902 4，基于被试者的分类准确率为0.933 3，比RBD临床筛查问卷效果更好 。.
Keywords: Parkinson’s disease; few-channel scalp electroencephalogram; intelligence-aided diagnosis; rapid eye movement sleep behavior disorder; time-frequency deep network.