A hierarchical classification method for automatic sleep scoring using multiscale entropy features and proportion information of sleep architecture

• Published in: Biocybernetics and biomedical engineering Vol. 37; no. 2; pp. 263 - 271
• Main Authors: Tian, Pan, Hu, Jie, Qi, Jin, Ye, Xian, Che, Datian, Ding, Ying, Peng, Yinghong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2017
• Subjects: Advanced Basic Science; Hierarchical classification; Internal Medicine; Multiscale entropy; Polysomnographic; Proportion information; Sleep scoring; Polysomnographic; Multiscale entropy; Sleep scoring; Proportion information; Hierarchical classification
• ISSN: 0208-5216
• DOI: 10.1016/j.bbe.2017.01.005

Sleep scoring is a critical step in medical researches and clinical applications. Traditional visual scoring method is based on the processing of physiological signals, such as electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG), which is a time consuming and subjective procedure. It is an urgent task to develop an effective method for automatic sleep scoring. This paper presents a hierarchical classification method for automatic sleep scoring by combining multiscale entropy features with the proportion information of the sleep architecture. Based on a three-layer classification scheme, sleep is categorized into five stages (Awake, S1, S2, SWS and REM). Specifically, the first layer is a standard SVM which performs classification between Awake and Sleep, while the second and third layers are implemented by combining probabilistic output SVM with proportion-based clustering. Multiscale entropy (MSE) from electroencephalogram (EEG) is extracted to represent signal characteristics in multiple temporal scales. The proposed method is evaluated with 20 sleep recordings, including 10 subjects with mild difficulty falling asleep and 10 healthy subjects. The overall accuracy of the proposed method is 91.4%. Compared with traditional methods, the classification accuracy of the proposed method is more balanced and the global performance is much better. The dataset includes both healthy subjects and subjects with sleep disorders, which means the presented method has good generalization capacity. Experimental results demonstrate the feasibility of the attempt to introduce proportion information into automatic sleep scoring.