A simple sleep stage identification technique for incorporation in inexpensive electronic sleep screening devices

This paper investigates pattern recognition techniques for identification of sleep stages based purely on respiratory signals. It focuses on computationally simplistic methods, which can be implemented on an inexpensive microprocessor in a low-cost and comfortable home-screening device for the detection of sleep-related disorders, such as obstructive sleep apnea. In spite of the fact that sleep stages are defined by measurements of electrical activity in the brain, there are quantifiable changes in the respiratory pattern which can be used to distinguish between sleep stages with a reasonable degree of accuracy. Multiple respiratory features were evaluated for their efficacy in classifying each 30 second epoch of a respiratory signal as Wake, Non-REM, or REM sleep. Both linear and naive-Bayes classifiers were comparatively tested on nasal and abdominal respiration signals collected from MIT-BIH Polysomnographic database, but optimal results were achieved using a naive-Bayes classifier. The findings of this study support the feasibility of respiratory-based sleep stage classification, which can be refined to a technique accurate enough for inexpensive sleep monitoring devices.