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BackgroundApproximately 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.MethodThe 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.ResultsOverall 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%.ConclusionThe results of the experiments demonstrate that the proposed method significantly enhances the recognition rate when compared with prior studies.

Health care researchers have recently developed several home-based sleep monitoring systems and mobile applications that support healthy sleep by monitoring a user's sleep environment, activities, or sleep quality. This study describes the design and evaluation of an electrooculogram (EOG)-based system for an automatic sleep monitoring. Compared with polysomnogram or electroencephalogram recordings, EOG has the advantage of easy placement and can be operated by the users themselves. We also design an intelligent eye mask that is user friendly for measuring sleep stage and quality. Two user experiments were carried out to demonstrate that the proposed system produces valid measurements of sleep stage and sleep quality and has good usability and reliability while not disturbing sleep significantly. These findings suggest that our system can also be applied to long-term sleep monitoring or sleep environment control to improve the user's sleep quality and make sleep more comfortable.

The results suggest that the proposed wearable actigraphy system is practical for the in-home screening of objective sleep measurements and objective evaluation of sleep improvement after treatment.