title: Automatic sleep staging using multi-dimensional feature extraction and multi-kernel fuzzy support vector machine.
abstract: This paper employed the clinical Polysomnographic (PSG) data, mainly including all-night Electroencephalogram (EEG), Electrooculogram (EOG) and Electromyogram (EMG) signals of subjects, and adopted the American Academy of Sleep Medicine (AASM) clinical staging manual as standards to realize automatic sleep staging. Authors extracted eighteen different features of EEG, EOG and EMG in time domains and frequency domains to construct the vectors according to the existing literatures as well as clinical experience. By adopting sleep samples self-learning, the linear combination of weights and parameters of multiple kernels of the fuzzy support vector machine (FSVM) were learned and the multi-kernel FSVM (MK-FSVM) was constructed. The overall agreement between the experts' scores and the results presented was 82.53%. Compared with previous results, the accuracy of N1 was improved to some extent while the accuracies of other stages were approximate, which well reflected the sleep structure. The staging algorithm proposed in this paper is transparent, and worth further investigation.
authors.affiliation: School of Engineering, Sun Yat-sen University, Guangdong 510006, China Jinan University, Guangdong 510632, China.
authors.forename: Yanjun
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Sleep-disordered Breathing Center of the 6th affiliated hospital, Sun Yat-Sen University, Guangdong 510655, China.
authors.forename: Xiangmin
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Jinan University, Guangdong 510632, China.
authors.forename: Wenhui
authors.initials: W
authors.lastname: Liu
authors.affiliation: School of Engineering, Sun Yat-sen University, Guangdong 510006, China.
authors.forename: Yuxi
authors.initials: Y
authors.lastname: Luo
authors.affiliation: Sleep-disordered Breathing Center of the 6th affiliated hospital, Sun Yat-Sen University, Guangdong 510655, China.
authors.forename: Enjia
authors.initials: E
authors.lastname: Yu
authors.affiliation: School of Engineering, Sun Yat-sen University, Guangdong 510006, China.
authors.forename: Keju
authors.initials: K
authors.lastname: Zou
authors.affiliation: School of Engineering, Sun Yat-sen University, Guangdong 510006, China.
authors.forename: Xiaoliang
authors.initials: X
authors.lastname: Liu
mesh_terms: D000328:Adult; D000368:Aged; D004568:Electrodiagnosis / Q000379:methods; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D008875:Middle Aged; D017286:Polysomnography / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages / Q000502:physiology*; D060388:Support Vector Machine
keywords: electroencephalogram (EEG); electromyogram (EMG); electrooculogram (EOG); multi-kernel fuzzy support vector machine (MK-FSVM); polysomnographic (PSG); sleep staging