A Cross-Modal Autoencoder for Contactless Electrocardiography Monitoring Using Frequency-Modulated Continuous Wave Radar

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Kai-Chun Liu;Sheng-Yu Peng;Yu Tsao;Che-Yu Liu;Zhu-An Chen;Zong Han Han;Wen-Chi Chen;Po-Quan Hsieh;You-Jin Li;Yu-Juei Hsu;Shun-Neng Hsu
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引用次数: 0

Abstract

While traditional electrocardiogram (ECG) monitoring provides vital clinical information, its electrode-based setup restricts patient movement. To address this limitation, contactless ECG monitoring using frequency-modulated continuous-wave (FMCW) radar and deep learning has been developed. However, such approaches face challenges owing to the limited availability of training data and inherent discrepancies between radar and ECG signals. This article introduces a novel approach to transforming high-fidelity ECG signals from millimeter-wave (mmWave) radar signals reflecting cardiac mechanical activity. The proposed method uses a cascade framework with a cross-modal autoencoder trained using joint waveforms, spectrograms, and deep feature losses. This strategy enables the model to leverage a pretrained ECG-to-ECG autoencoder and a cardiac event (CE) predictor for learning general ECG representations while simultaneously capturing time- and frequency-domain features from limited data. We evaluated the effectiveness of the proposed autoencoder model in terms of signal quality and CE integrity using ablation studies on data from 20 healthy participants. The model achieved high transformation accuracy with a cross correlation of 0.914 and average timing errors below 31 ms for critical ECG features. These findings demonstrate the feasibility and effectiveness of the proposed FMCW radar-based contactless ECG monitoring method, particularly in overcoming the limitations imposed by small datasets and domain discrepancies.
调频连续波雷达非接触式心电图监测的跨模态自动编码器
虽然传统的心电图(ECG)监测提供重要的临床信息,但其基于电极的设置限制了患者的活动。为了解决这一限制,使用调频连续波(FMCW)雷达和深度学习的非接触式心电监测已经被开发出来。然而,由于训练数据的可用性有限以及雷达和心电信号之间固有的差异,这种方法面临挑战。本文介绍了一种将反映心脏机械活动的毫米波(mmWave)雷达信号转换成高保真心电图信号的新方法。所提出的方法使用级联框架和使用联合波形、频谱图和深度特征损失训练的跨模态自编码器。该策略使模型能够利用预训练的ECG-to-ECG自动编码器和心脏事件(CE)预测器来学习一般ECG表示,同时从有限的数据中捕获时域和频域特征。我们利用来自20名健康参与者的消融研究数据,从信号质量和CE完整性方面评估了所提出的自编码器模型的有效性。该模型具有较高的变换精度,相关系数为0.914,对关键心电特征的平均时序误差小于31 ms。这些发现证明了所提出的基于FMCW雷达的非接触式心电监测方法的可行性和有效性,特别是在克服小数据集和域差异的限制方面。
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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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