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


METHODS
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%.


CONCLUSIONS
The results of the experiments demonstrate that the proposed method significantly enhances the recognition rate when compared with prior studies.
Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan, ROC.
Shing-Tai
ST
Pan
Chih-En
CE
Kuo
Jian-Hong
JH
Zeng
Sheng-Fu
SF
Liang
D001331:Automation; D004569:Electroencephalography; D004576:Electromyography; D004585:Electrooculography; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages; D055815:Young Adult