1 0 0 0 OA マイクロ波レーダを用いた体動と呼吸間隔ゆらぎ指標による睡眠段階推定
- 著者
- 佐々木 紀幸 香川 正幸 鈴村 和季 松井 岳巳
- 出版者
- 公益社団法人 日本生体医工学会
- 雑誌
- 生体医工学 (ISSN:1347443X)
- 巻号頁・発行日
- vol.53, no.4, pp.209-216, 2015-08-10 (Released:2015-12-10)
- 参考文献数
- 15
- 被引用文献数
- 1
Disturbed sleep has become more common in recent years. To improve the quality of sleep, undergoing sleep observation has gained interest in an attempt to resolve possible problems. Additionally, the classification of real-time sleep states without interference is needed in nursing and medical settings. In this paper, we discuss a non-restrictive and non-contact method for estimating real-time sleep stages and report its potential applications and problems. The system we used measured body movements and respiratory signals of a person while sleeping using dual 24-GHz microwave radars placed underneath the mattress. We first determined a body movement index to identify wake and sleep, and fluctuation indices of respiratory intervals to indicate sleep stages. The data of movements and respiratory signals derived from 11 healthy university students were used for feature selection and training of the classification model parameters. For identifying wake and sleep, the rate of agreement between the body movement index and the result using the R & K method as reference was 83.5±6.3%. Five-minute standard deviation, a fluctuation index of respiratory intervals, had a high degree of contribution and showed a significant difference (p<0.001) among three sleep stages (REM, LIGHT, DEEP). Th e degree of contribution of the 5-min fractal dimension of respiratory intervals, a fluctuation index, was not as high as expected but showed a significant difference (p<0.05) between REM and DEEP. For validation, we applied canonical discriminant analysis to classify wake or sleep and to estimate the three sleep stages in two additional university students. The accuracy was 79.3% and 59.5% for classification of wake/sleep and 71.9% and 64.0% for estimation of three sleep stages. The novelty of this study includes measurements of body movements and respiration-induced body surface movements withh ighsensitivities using microwave radars, and systematic analysis of the indices of body movement and respiratory interval. The method allows easy measurement of sleep stages in nursing care and home settings and may be employed to increase the quality of sleep. Although we successfully estimated three sleep stages, the method requires further study to determine the five stages of sleep.