Abstract
In this study, we proposed a method for predicting state of slow-wave sleep based on respiratory waveforms. In our tests, we derived CV values for the amplitude of breathing movement for 5-minute periods, and evaluated three sleep states: REM (Rapideye movement) sleep, WAKE (static supine position, eyes closed), and SWS (Slow-wave sleep), and found that there was a large difference in these amplitude values. Next, we used entire night data for the same subjects, and predicted the SWS periods using the CVvalues. Comparing these results with the results of sleep depth as judged from a sleep polygraph, we confirmed that SWS periods could be predicted with a high level of accuracy. The above results indicate that it is possible to predict state of slow-wave using onlybreathing waveforms during sleep.
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