基于少导联脑电和时频深度网络的帕金森病伴快速眼动睡眠行为障碍智能辅助诊断方法  

Intelligence-aided diagnosis of Parkinson’s disease with rapid eye movement sleep behavior disorder based on few-channel electroencephalogram and time-frequency deep network

作　　者：仲伟峰[1,6] 李志 刘燕 程晨晨[2,3] 王悦 张丽 徐淑兰[5] 蒋旭[5] 朱骏 戴亚康[2] ZHONG Weifeng;LI Zhi;LIU Yan;CHENG Chenchen;WANG Yue;ZHANG Li;XU Shulan;JIANG Xu;ZHU Jun;DAI Yakang(School of Automation,Harbin University of Science and Technology,Harbin 150080,P.R.China;Suzhou Institute of Biomedical Engineering and Technology,Chinese Academy of Sciences,Suzhou,Jiangsu 215163,P.R.China;School of Mechanical and Power Engineering,Harbin University of Science and Technology,Harbin 150080,P.R.China;Jinan Guoke Medical Engineering Technology Development Co.,LTD,Jinan 250102,P.R.China;Nanjing Brain Hospital Affiliated to Nanjing Medical University,Nanjing 210029,P.R.China;Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration,Harbin 150080,P.R.China)

机构地区：[1]哈尔滨理工大学自动化学院,哈尔滨150080 [2]中国科学院苏州生物医学工程技术研究所,江苏苏州215163 [3]哈尔滨理工大学机械动力工程学院,哈尔滨150080 [4]济南国科医工科技发展有限公司,济南250102 [5]南京医科大学附属脑科医院,南京210029 [6]黑龙江省复杂智能系统与集成重点实验室,哈尔滨150080

出　　处：《生物医学工程学杂志》2021年第6期1043-1053,共11页Journal of Biomedical Engineering

基　　金：国家自然科学基金(61801476,61971413);山东省自然科学基金(ZR2020QF018,ZR2020QF019);济南市创新团队(2018GXRC017)。

摘　　要：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0.9024,基于被试者的分类准确率为0.9333,比RBD临床筛查问卷效果更好。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 0.9024 in the validation set.The accuracy of the subject-based classification was 0.9333 in the test set.Compared with the RBD screening questionnaire(RBDSQ),the novel approach has clinical application value.

关 键 词：少导联脑电 时频深度网络 帕金森病 快速眼动睡眠行为障碍 智能辅助诊断 

分 类 号：R742.5[医药卫生—神经病学与精神病学] R740[医药卫生—临床医学]