Exploration of Mode Decomposition for Concurrent Cardiopulmonary Monitoring using Dual Radar

Arindam Ray, A. Khasnobish, Smriti Rani, A. Chowdhury, T. Chakravarty
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引用次数: 2

Abstract

Cardiopulmonary monitoring involves surveilling the important physiological parameters of an individual like the breathing rate (BR) and the heart rate (HR). This paper uses a simple, off-the-shelf dual multifrequency Continuous Wave (CW) radar setup to monitor the BR and HR of a static individual. The source separation problem of extracting the HR signal in presence of a higher amplitude BR signal poses a huge challenge and has been effectively solved by using an optimal channel selection process and the Variational Mode Decomposition (VMD) algorithm in this paper. Frequency extraction from the nonstationary signal modes produced by VMD has been performed by using the Fourier-Bessel transform to extract precise frequency information. Results show that the proposed system is accurate and outperforms other existing mode decomposition methods like Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) with a mean absolute error of 5.1±5.4 with respect to the number of heartbeats per minute and an accuracy of 95.87%(±4.9) with respect to the number of breaths per minute.
双雷达并发心肺监测模式分解的探索
心肺监测包括监测个体的重要生理参数,如呼吸频率(BR)和心率(HR)。本文使用一种简单的、现成的双多频连续波(CW)雷达装置来监测静态个体的BR和HR。本文采用最优信道选择过程和变分模态分解(VMD)算法有效地解决了在高幅值BR信号存在下提取HR信号的源分离问题。利用傅里叶-贝塞尔变换从VMD产生的非平稳信号模式中提取精确的频率信息。结果表明,该系统具有较高的准确率,优于经验模态分解(EMD)和集成经验模态分解(EEMD)等现有模态分解方法,每分钟心跳次数的平均绝对误差为5.1±5.4,每分钟呼吸次数的平均绝对误差为95.87%(±4.9)。
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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