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睡眠期间呼吸节律和脉搏率的实时监测。

Real-time monitoring of respiration rhythm and pulse rate during sleep.

作者信息

Zhu Xin, Chen Wenxi, Nemoto Tetsu, Kanemitsu Yumi, Kitamura Kei-ichiro, Yamakoshi Ken-ichi, Wei Daming

机构信息

Graduate Department of Information Systems, University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan.

出版信息

IEEE Trans Biomed Eng. 2006 Dec;53(12 Pt 1):2553-63. doi: 10.1109/TBME.2006.884641.

Abstract

A noninvasive and unconstrained real-time method to detect the respiration rhythm and pulse rate during sleep is presented. By employing the a 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 26 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.

摘要

本文提出了一种用于检测睡眠期间呼吸节律和脉搏率的无创且无约束的实时方法。通过采用小波变换(WT)的“空洞”算法,可以从放置在枕头下的压力传感器采集的压力信号中实时监测呼吸节律和脉搏率。用于呼吸节律检测的波形源自第26级近似,而用于脉搏率检测的波形则通过组合第2(4)级和第2(5)级细节来合成。为了最小化数据处理中的延迟并实现最高的实时性能,通过直接使用源自WT近似和细节分量的波形而无需重建过程来估计呼吸节律和脉搏率。该方法通过从13名健康受试者收集的数据进行评估。与用于脉搏率检测的手指光电体积描记法检测结果相比,灵敏度和阳性预测值分别为99.17%和98.53%。同样,对于呼吸节律,与来自鼻热敏电阻信号的检测结果相比,结果分别为95.63%和95.42%。本研究表明,所提出的方法有望用于呼吸节律和脉搏率监测器,以实时监测睡眠期间与睡眠相关的疾病。

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