Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6484232 | Biocybernetics and Biomedical Engineering | 2017 | 9 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
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Bioengineering
Authors
Pan Tian, Jie Hu, Jin Qi, Xian Ye, Datian Che, Ying Ding, Yinghong Peng,