A model-based monitor of human sleep stages
Biological Cybernetics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
IEEE Transactions on Information Technology in Biomedicine
Nonconstrained sleep monitoring system and algorithms using air-mattress with balancing tube method
IEEE Transactions on Information Technology in Biomedicine
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The judgment standards of R-K method include ambiguities and are thus compensated by subjective interpretations of sleep-stage scorers. This paper presents a novel method to compensate uncertainties in judgments by the subjective interpretations by the sleep-model estimation approach and by describing the judgments in probabilistic terms. Kalman filter based on the two sleep models with no body movement and with body movement was designed. Sleep stages judged by three different scorers were rejudged by the filter. The two sleep models were stochastically estimated from biosignals from 15 nights' data and the rejudged scores by the filter were evaluated by the data from 5 nights. The average values of kappa statistics, which show the degree of agreement, were 0.85, 0.89, and 0.81, respectively, for the original sleep stages. Because the new method provides probabilities on how surely the sleep belongs to each sleep stage, we were able to determine the most, second most, and third most probable sleep stage. The kappa statistics between the most probable sleep stages were improved to 0.90, 0.93, and 0.84, respectively. Those of sleep stages determined from the most and second most probable were 0.92, 0.94, and 0.89 and those from the most, second most, and third most probable were 0.95, 0.97, and 0.92. The sleep stages estimated by the filter are expressed by probabilisticmanner, which are more reasonable in expression than those given by deterministic manner. The expression could compensate the uncertainties in each judgmentsand thus weremore accurate than the direct judgments.