Automatic identification of sleep stage is an important step in a sleep study. In this paper, we propose a hybrid automatic sleep stage scoring approach, named HyCLASSS, based on single channel electroencephalogram (EEG). HyCLASSS, for the first time, leverages both signal and stage transition features of human sleep for automatic identification of sleep stages. HyCLASSS consists of two parts: A random forest classifier and correction rules. Random forest classifier is trained using 30 EEG signal features, including temporal, frequency, and nonlinear features. The correction rules are constructed based on stage transition feature, importing the continuity property of sleep, and characteristic of sleep stage transition. Compared with the gold standard of manual scoring using Rechtschaffen and Kales criterion, the overall accuracy and kappa coefficient applied on 198 subjects has reached 85.95% and 0.8046 in our experiment, respectively. The performance of HyCLASS compared favorably to previous work, and it could be integrated with sleep evaluation or sleep diagnosis system in the future.
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This work was supported in part by the University of Kentucky, in part by the Center for Clinical and Translational Science Award UL1TR001998, in part by the National Sleep Research Resource under Grant NHLBI R24 HL114473, and in part by the National Science Foundation under Grant 1626364
Li, Xiaojin; Cui, Licong; Tao, Shiqiang; Chen, Jing; Zhang, Xiang; and Zhang, Guo-Qiang, "HyCLASSS: A Hybrid Classifier for Automatic Sleep Stage Scoring" (2017). Institute for Biomedical Informatics Faculty Publications. 6.