基于使用雷达非接触式传感器的高阶谐波峰值选择方法的精确心率和呼吸率检测
Accurate Heart Rate and Respiration Rate Detection Based on a Higher-Order Harmonics Peak Selection Method Using Radar Non-Contact Sensors.
作者信息
Xu Hongqiang, Ebrahim Malikeh P, Hasan Kareeb, Heydari Fatemeh, Howley Paul, Yuce Mehmet Rasit
机构信息
Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC 3800, Australia.
Planet Innovation, Box Hill, VIC 3128, Australia.
出版信息
Sensors (Basel). 2021 Dec 23;22(1):83. doi: 10.3390/s22010083.
Vital signs such as heart rate and respiration rate are among the most important physiological signals for health monitoring and medical applications. Impulse radio (IR) ultra-wideband (UWB) radar becomes one of the essential sensors in non-contact vital signs detection. The heart pulse wave is easily corrupted by noise and respiration activity since the heartbeat signal has less power compared with the breathing signal and its harmonics. In this paper, a signal processing technique for a UWB radar system was developed to detect the heart rate and respiration rate. There are four main stages of signal processing: (1) clutter removal to reduce the static random noise from the environment; (2) independent component analysis (ICA) to do dimension reduction and remove noise; (3) using low-pass and high-pass filters to eliminate the out of band noise; (4) modified covariance method for spectrum estimation. Furthermore, higher harmonics of heart rate were used to estimate heart rate and minimize respiration interference. The experiments in this article contain different scenarios including bed angle, body position, as well as interference from the visitor near the bed and away from the bed. The results were compared with the ECG sensor and respiration belt. The average mean absolute error (MAE) of heart rate results is 1.32 for the proposed algorithm.
心率和呼吸率等生命体征是健康监测和医疗应用中最重要的生理信号之一。脉冲无线电(IR)超宽带(UWB)雷达成为非接触式生命体征检测中的重要传感器之一。由于心跳信号的功率与呼吸信号及其谐波相比更小,心脏脉搏波很容易被噪声和呼吸活动干扰。本文开发了一种用于UWB雷达系统的信号处理技术,以检测心率和呼吸率。信号处理主要有四个阶段:(1)杂波去除,以减少来自环境的静态随机噪声;(2)独立成分分析(ICA),用于降维和去除噪声;(3)使用低通和高通滤波器消除带外噪声;(4)改进的协方差方法进行频谱估计。此外,利用心率的高次谐波来估计心率并最小化呼吸干扰。本文的实验包含不同场景,包括床的角度、身体位置,以及床边和远离床边访客的干扰。将结果与心电图传感器和呼吸带进行了比较。所提算法的心率结果平均平均绝对误差(MAE)为1.32。