利用独立分量分析和经验模式分解分离同时进行的双被试心跳波形

0 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE microwave and wireless technology letters Pub Date : 2024-07-08 DOI:10.1109/LMWT.2024.3420253
Jahid Hasan Chowdhury;Md. Shihab;Sourav Kumar Pramanik;Md. Shafkat Hossain;Kaisari Ferdous;Md. Shahriar;Shekh M. M. Islam
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引用次数: 0

摘要

使用连续波(CW)微波多普勒雷达进行生命体征监测因其结构简单、信号处理链较少而日益受到关注。现有文献侧重于在被测物在角间距限制内时利用连续波雷达的到达方向(DOA)技术。然而,当两个被测物的角度间距超过雷达的 DOA 估算极限时,DOA 技术就会失效。为解决这一难题,本研究工作重点测试了两种信号处理方法(经验模式分解法(EMD)和特征矩阵联合近似对角化独立分量分析法(ICA-JADE))在受试者处于 CW 雷达波束宽度范围内的实验场景中的有效性。使用两种不同的信号处理方法分离出单个心跳波形后,将其与 Biopac 心电图记录的心跳信号进行比较。实验结果表明,在所有重复测量中,ICA-JADE 方法以 92.57% 的准确率超越了 EMD 技术。
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Separation of Heartbeat Waveforms of Simultaneous Two-Subjects Using Independent Component Analysis and Empirical Mode Decomposition
Vital sign monitoring using continuous wave (CW) microwave Doppler radar is gaining attention due to its simpler architecture and fewer signal processing chains. Existing literature focuses on utilizing the direction of arrival (DOA) technique of CW radar when the subjects are within the angular spacing limit. However, when two subjects cross the angular spacing limit for DOA estimation of the radar then the DOA technique becomes ineffective. To address this challenge, this research work focuses on testing the efficacy of two signal processing approaches [empirical mode decomposition (EMD) and independent component analysis with the joint approximation diagonalization of the eigenmatrices (ICA-JADE)] for the experimental scenarios when the subjects are within the beamwidth of the CW radar. After isolating the individual heartbeat waveforms using two different signal processing approaches it was compared with the Biopac ECG recorded heartbeat signal. Experimental results demonstrated that the ICA-JADE method superseded the performance of the EMD technique with an accuracy of 92.57% in all repeated measurements.
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Table of Contents IEEE Microwave and Wireless Technology Letters Information for Authors IEEE Microwave and Wireless Technology Letters publication TechRxiv: Share Your Preprint Research with the World IEEE Open Access Publishing
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