A Novel Detection Method for Heart Rate Variability and Sleep Posture Based on a Flexible Sleep Monitoring Belt

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Chunhua He;Shuibin Liu;Zewen Fang;Heng Wu;Maojin Liang;Songqing Deng;Juze Lin
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Abstract

Heart rate variability (HRV) is an important indicator for assessing the function of the cardiac autonomic nervous system (ANS), and it is important for early detection and prevention of cardiovascular diseases, stress management, and mental health. Besides, different sleep postures have different effects on respiration and ventilation, and inappropriate sleep postures may lead to organ compression and obstructive sleep apnea (OSA). Therefore, HRV and sleep posture detection are very significant. However, there is a lack of the high-comfortable, low-cost, and high-accuracy detection methods. In this article, a novel detection method for HRV and sleep posture based on a flexible sleep monitoring belt (FSMB) is proposed. The test platform, including an FSMB and a bioelectrical signal acquisition circuit (BSAC), as well as the test flow, is described in detail. The BSAC composed of a series of amplifiers and filters is designed to acquire the electrocardiography (ECG) signal, while the FSMB mainly composed of a MEMS inertial measurement unit (IMU) and a pressure sensor array is designed to acquire the ballistocardiography (BCG) or gyrocardiography (GCG) signal. Besides, the HRV features of ECG, BCG, and GCG signals are extracted by the wavelet packet transform (WPT) analysis, and the short-time energies of the triaxial accelerations and angular velocities are extracted as the features for sleep posture detection. For facilitating the realization with edge computing, a lightweight convolutional neural network (CNN) model is proposed to recognize the sleep posture. The experimental results indicate that the detection accuracy of HRV with BCG signal is slightly bigger than that with GCG signal, reaching 91.1% compared with the result of ECG signal. In addition, the detection accuracy of sleep posture with the proposed CNN model achieves 96.44%. Therefore, the proposed detection method of HRV and sleep posture based on the FSMB is effective and feasible.
一种基于柔性睡眠监测带的心率变异性和睡眠姿势检测新方法
心率变异性(HRV)是评估心脏自主神经系统(ANS)功能的重要指标,对心血管疾病的早期发现和预防、压力管理和心理健康具有重要意义。此外,不同的睡姿对呼吸和通气的影响也不同,不恰当的睡姿可能导致器官压迫和阻塞性睡眠呼吸暂停(OSA)。因此,HRV和睡眠姿势检测是非常重要的。然而,目前还缺乏高舒适、低成本、高精度的检测方法。本文提出了一种基于柔性睡眠监测带(FSMB)的HRV和睡眠姿势检测方法。详细介绍了FSMB和生物电信号采集电路(BSAC)组成的测试平台以及测试流程。BSAC由一系列放大器和滤波器组成,用于采集心电图信号;FSMB主要由MEMS惯性测量单元(IMU)和压力传感器阵列组成,用于采集心电图(BCG)或心电图(GCG)信号。利用小波包变换(WPT)提取ECG、BCG和GCG信号的HRV特征,提取三轴加速度和角速度的短时能量作为睡眠姿势检测的特征。为了便于边缘计算实现,提出了一种轻量级的卷积神经网络(CNN)模型来识别睡眠姿势。实验结果表明,与心电信号相比,BCG信号对HRV的检测准确率略高于GCG信号,达到91.1%。此外,本文提出的CNN模型对睡眠姿势的检测准确率达到96.44%。因此,提出的基于FSMB的HRV和睡眠姿势检测方法是有效可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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