A transition-constrained discrete hidden Markov model for automatic sleep staging

Shing Tai Pan, Chih En Kuo, Jian Hong Zeng, Sheng Fu Liang

研究成果: Article同行評審

66 引文 斯高帕斯(Scopus)


Background: Approximately one-third of the human lifespan is spent sleeping. To diagnose sleep problems, all-night polysomnographic (PSG) recordings including electroencephalograms (EEGs), electrooculograms (EOGs) and electromyograms (EMGs), are usually acquired from the patient and scored by a well-trained expert according to Rechtschaffen & Kales (R&K) rules. Visual sleep scoring is a time-consuming and subjective process. Therefore, the development of an automatic sleep scoring method is desirable.Method: The EEG, EOG and EMG signals from twenty subjects were measured. In addition to selecting sleep characteristics based on the 1968 R&K rules, features utilized in other research were collected. Thirteen features were utilized including temporal and spectrum analyses of the EEG, EOG and EMG signals, and a total of 158 hours of sleep data were recorded. Ten subjects were used to train the Discrete Hidden Markov Model (DHMM), and the remaining ten were tested by the trained DHMM for recognition. Furthermore, the 2-fold cross validation was performed during this experiment.Results: Overall agreement between the expert and the results presented is 85.29%. With the exception of S1, the sensitivities of each stage were more than 81%. The most accurate stage was SWS (94.9%), and the least-accurately classified stage was S1 (<34%). In the majority of cases, S1 was classified as Wake (21%), S2 (33%) or REM sleep (12%), consistent with previous studies. However, the total time of S1 in the 20 all-night sleep recordings was less than 4%.Conclusion: The results of the experiments demonstrate that the proposed method significantly enhances the recognition rate when compared with prior studies.

期刊Biomedical engineering online
出版狀態Published - 2012 8月 21

All Science Journal Classification (ASJC) codes

  • 放射與超音波技術
  • 生物材料
  • 生物醫學工程
  • 放射學、核子醫學和影像學


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