文献类型：期刊文献

英文题名：A Comparison Study on Multidomain EEG Features for Sleep Stage Classification

作者：Zhang, Yu[1,2];Wang, Bei[1,2];Jing, Jin[1,2];Zhang, Jian[2];Zou, Junzhong[2];Nakamura, Masatoshi[3]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Dept Automat, Shanghai, Peoples R China;[3]Inst Adv Res & Educ, Res Inst Syst Control, Saga, Japan

年份：2017

卷号：2017

外文期刊名：COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE

收录：;EI(收录号：20180804822174);WOS:【SCI-EXPANDED(收录号:WOS:000415875000001)】；

基金：The authors are grateful to Dr. Fusae Kawana, Department of Clinical Physiology, Toranomon Hospital, Japan, for the technical help in sleep stage inspection. This research was financially supported by the National Natural Science Foundation of China under Grant 61773164, the Shanghai Natural Science Foundation under Grant 16ZR1407500, and the Fundamental Research Funds for the Central Universities under Grant 222201717006.

语种：英文

外文关键词：Time domain analysis - Sleep research - Nonlinear analysis - Biomedical signal processing - Feature extraction - Patient treatment - Extraction - Frequency domain analysis

摘要：Feature extraction from physiological signals of EEG (electroencephalogram) is an essential part for sleep staging. In this study, multidomain feature extraction was investigated based on time domain analysis, nonlinear analysis, and frequency domain analysis. Unlike the traditional feature calculation in time domain, a sequence merging method was developed as a preprocessing procedure. The objective is to eliminate the clutter waveform and highlight the characteristic waveform for further analysis. The numbers of the characteristic activities were extracted as the features from time domain. The contributions of features from different domains to the sleep stages were compared. The effectiveness was further analyzed by automatic sleep stage classification and compared with the visual inspection. The overnight clinical sleep EEG recordings of 3 patients after the treatment of Continuous Positive Airway Pressure (CPAP) were tested. The obtained results showed that the developed method can highlight the characteristic activity which is useful for both automatic sleep staging and visual inspection. Furthermore, it can be a training tool for better understanding the appearance of characteristic waveforms from raw sleep EEG which is mixed and complex in time domain.