Real-Time Monitoring of Respiration Rhythm and Pulse Rate During Sleep

• Published in: IEEE transactions on biomedical engineering Vol. 53; no. 12; pp. 2553 - 2563
• Main Authors: Zhu, Xin, Chen, Wenxi, Nemoto, Tetsu, Kanemitsu, Yumi, Kitamura, Kei-ichiro, Yamakoshi, Ken-ichi, Wei, Daming
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2006; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Biomedical measurements; Cardiac disease; Cardiovascular diseases; Computer Systems; Condition monitoring; Data processing; Diagnosis, Computer-Assisted - methods; Female; Heart rate; Heart Rate - physiology; Heart rate measurement; Humans; Information systems; Male; Manometry - methods; Polysomnography - methods; Pressure; pulse rate; real-time monitor; Respiration; respiration rhythm; Respiratory Mechanics - physiology; Rhythm; Sleep; Sleep - physiology; sleep monitor; Strain measurement; wavelet transformation; à trous algorithm
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2006.884641

A noninvasive and unconstrained real-time method to detect the respiration rhythm and pulse rate during sleep is presented. By employing the agrave trous algorithm of the wavelet transformation (WT), the respiration rhythm and pulse rate can be monitored in real-time from a pressure signal acquired with a pressure sensor placed under a pillow. The waveform for respiration rhythm detection is derived from the 2 6 scale approximation, while that for pulse rate detection is synthesized by combining the 2 4 and 2 5 scale details. To minimize the latency in data processing and realize the highest real-time performance, the respiration rhythm and pulse rate are estimated by using waveforms directly derived from the WT approximation and detail components without the reconstruction procedure. This method is evaluated with data collected from 13 healthy subjects. By comparing with detections from finger photoelectric plethysmograms used for pulse rate detection, the sensitivity and positive predictivity were 99.17% and 98.53%, respectively. Similarly, for respiration rhythm, compared with detections from nasal thermistor signals, results were 95.63% and 95.42%, respectively. This study suggests that the proposed method is promising to be used in a respiration rhythm and pulse rate monitor for real-time monitoring of sleep-related diseases during sleep