possibility of predicting sleep state with a focus on respiratory waveforms

There is a close relationship between quality of sleep and general health, and we believe that long-term monitoring of sleep state is an important method of evaluating the subject's state of health.The most common method of monitoring sleep state measures biological information such as EEG, eye movement, and EMG, and combines this information to identify the depth of sleep. Given this situation, there has been a demand for equipment that will enable more convenient measurement of sleep state in the home.In recent years, methods have been established for measuring breathing and heart rate during sleep without restraining the subject. Although it has been known through past research that heart rate has been the main index used for predicting sleep state, and breathing has rarely been made the subject of study. In the current research, we thus focused on breathing waveforms, and proposed a method for predicting sleep state based on these waveforms. In our tests, we took measurements of five healthy males in their 20s using a sleep polygraph and measuring the breathing movements in the chest. We derived CV values for the amplitude of breathing movement for 5-minute periods, and evaluated three sleep states: REM sleep, arousal from sleep (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 data throughout an entire night for the same subjects, and predicted the SWS periods using the CV values for 5-minute periods. 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 (average sensitivity: 0.63; average characteristics: 0.90).The above results indicate that it is possible to predict sleep states using only breathing waveforms during sleep.