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用于基于心冲击图的心率和呼吸率估计的气垫系统。

Air-mattress system for ballistocardiogram-based heart rate and breathing rate estimation.

作者信息

Lin Chun-Liang, Sun Zhen-Tai, Chen Yang-Yi

机构信息

Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan.

出版信息

Heliyon. 2022 Dec 29;9(1):e12717. doi: 10.1016/j.heliyon.2022.e12717. eCollection 2023 Jan.

Abstract

Sleep-related problems are widespread. Numerous devices for sleep monitoring are increasingly available, including smartwatches, sleep monitoring rings, etc. These devices accumulate and analyze a substantial quantity of physiological data. In this study, we develop a smart air-mattress system that can effectively aid health measurements. The proposed system adopts an air-mattress system to detect subtle changes in pressure and thereby collect micro physiological signals, including ballistocardiography (BCG) and breathing signals. The system uses ultrasonic signals to detect the subject's turning movements. To increase the signal recognition accuracy, the BCG signal is processed effectively to reduce noise interference engendered by the body movement during sleep and is processed using regression analysis for heart rate and breathing rate estimation. Accordingly, the proposed system is unconstrained and can be used to collect micro BCG signals, breathing signals, heart rate signals, and turning movements for the long-term health-care.

摘要

与睡眠相关的问题很普遍。越来越多的睡眠监测设备可供使用,包括智能手表、睡眠监测手环等。这些设备积累并分析大量生理数据。在本研究中,我们开发了一种能够有效辅助健康测量的智能气垫系统。所提出的系统采用气垫系统来检测压力的细微变化,从而收集包括心冲击图(BCG)和呼吸信号在内的微观生理信号。该系统使用超声波信号来检测受试者的翻身动作。为了提高信号识别准确率,对BCG信号进行有效处理,以减少睡眠期间身体运动产生的噪声干扰,并使用回归分析对心率和呼吸率进行估计。因此,所提出的系统不受限制,可用于长期医疗保健中收集微观BCG信号、呼吸信号、心率信号和翻身动作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fd9/9849971/4c8b9bd35047/gr9.jpg

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