HyCLASSS: A Hybrid Classifier for Automatic Sleep Stage Scoring

Xiaojin Li, Licong Cui, Shiqiang Tao, Jing Chen, Xiang Zhang, Guo Qiang Zhang

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)375-385
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Issue number2
StatePublished - Mar 2018

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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