ASVME

The breath rate (BR), heart rate (HR), breathing-breathing interval (BBI) and heart rate variability (HRV) are the critical vital sign parameters. In this article, a novel method named adaptive separation variational mode extraction algorithm (ASVME) is proposed to accurately monitor multi-variable vital signs (MVVS) at the same time with a frequency-modulated continuous wave (FMCW) radar system in practical scenarios. Firstly, a minimum variance distortionless response (MVDR) spectrum estimation algorithm is proposed to accurately locate respiratory and heartbeat components, which can effectively restrain the influence of respiratory harmonics on HR and R-R intervals (RRI) measurements. Subsequently, an adaptive variational mode extraction (AVME) algorithm is proposed to accurately extract respiratory waves and heartbeat waves after accurate frequency location. 呼吸率（BR）、心率（HR）、呼吸间隔（BBI）与心率变异性（HRV）均为关键生命体征参数。本文提出一种名为自适应分离变分模态提取算法（ASVME）的新型方法，可在实际场景中借助调频连续波（FMCW）雷达系统同时精准监测多变量生命体征（MVVS）。首先，本文提出最小方差无畸变响应（MVDR）谱估计算法，以精准定位呼吸与心跳分量，该方法可有效抑制呼吸谐波对心率及R波间期（RRI）测量的影响。随后，本文提出自适应变分模态提取（AVME）算法，在完成精准频率定位后，可精准提取呼吸波与心跳波。

搜集汇总

背景与挑战

背景概述

ASVME数据集是一个信号处理领域的标准数据集，提出了一种名为自适应分离变分模态提取算法（ASVME），用于通过FMCW雷达同时监测多变量生命体征（如呼吸率、心率等）。该算法结合MVDR频谱估计和自适应变分模态提取技术，旨在抑制呼吸谐波干扰并准确提取呼吸和心跳波形。