Unconstrained detection of respiration rhythm and pulse rate with one under-pillow sensor during sleep

A completely non-invasive and unconstrained method is proposed to detect respiration rhythm and pulse rate during sleep. By employing wavelet transformation (WT), waveforms corresponding to the respiration rhythm and pulse rate can be extracted from a pulsatile pressure signal acquired by a pressure...

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Bibliographic Details
Published in:Medical & biological engineering & computing 2005-03, Vol.43 (2), p.306-312
Main Authors: Chen, W, Zhu, X, Nemoto, T, Kanemitsu, Y, Kitamura, K, Yamakoshi, K
Format: Article
Language:English
Subjects:
Adult
Algorithms
Biomedical engineering
Female
Heart Rate - physiology
Humans
Male
Monitoring, Physiologic - methods
Respiration
Respiratory Physiological Phenomena
Sensors
Signal Processing, Computer-Assisted
Sleep
Sleep - physiology
Studies
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Summary:A completely non-invasive and unconstrained method is proposed to detect respiration rhythm and pulse rate during sleep. By employing wavelet transformation (WT), waveforms corresponding to the respiration rhythm and pulse rate can be extracted from a pulsatile pressure signal acquired by a pressure sensor under a pillow. The respiration rhythm was obtained by an upward zero-crossing point detection algorithm from the respiration-related waveform reconstructed from the WT 2(6) scale approximation, and the pulse rate was estimated by a peak point detection algorithm from the pulse-related waveform reconstructed from the WT 2(4) and 2(5) scale details. The finger photo-electric plethysmogram (FPP) and nasal thermistor signals were recorded simultaneously as reference signals. The reference pulse rate and respiration rhythm were detected with the peak and upward zero-crossing point detection algorithm. This method was verified using about 24 h of data collected from 13 healthy subjects. The results showed that, compared with the reference data, the average error rates were 3.03% false negative and 1.47% false positive for pulse rate detection in the extracted pulse waveform. Similarly, 4.58% false negative and 3.07% false positive were obtained for respiration rhythm detection in the extracted respiration waveform. This study suggests that the proposed method is suitable, in sleep monitoring, for the diagnosis of sleep apnoea or sudden death syndrome.
ISSN:0140-0118
1741-0444
DOI:10.1007/BF02345970