AirECG: Contactless Electrocardiogram for Cardiac Disease Monitoring via mmWave Sensing and Cross-domain Diffusion Model

Author:

Zhao Langcheng1ORCID,Lyu Rui1ORCID,Lei Hang1ORCID,Lin Qi1ORCID,Zhou Anfu1ORCID,Ma Huadong1ORCID,Wang Jingjia2ORCID,Meng Xiangbin2ORCID,Shao Chunli2ORCID,Tang Yida2ORCID,Chi Guoxuan3ORCID,Yang Zheng3ORCID

Affiliation:

1. State key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

2. Department of Cardiology, Peking University Third Hospital, Beijing, China

3. School of Software and BNRist, Tsinghua University, Beijing, China

Abstract

The electrocardiogram (ECG) has always served as a crucial biomedical examination for cardiac diseases monitoring and diagnosing. Typical ECG measurement requires attaching electrodes to the body, which is inconvenient for long-term monitoring. Recent wireless sensing maps wireless signals reflected from human chest into electrical activities of heart so as to reconstruct ECG contactlessly. While making great progress, we find existing works are effective only for healthy populations with normal ECG, but fall short when confronted with the most desired usage scenario: reconstructing ECG accurately for people with cardiac diseases such as atrial fibrillation, premature ventricular beat. To bridge the gap, we propose AirECG, which moves forward to reconstruct ECG for both healthy people and even cardiac patients with morbid ECG, i.e., irregular rhythm and anomalous ECG waveform, via contactless millimeter-wave sensing. To realize AirECG, we first custom-design a cross-domain diffusion model that can perform multiple iteration denoising inference, in contrast with the single-step generative models widely used in previous works. In this way, AirECG is able to identify and eliminate the distortion due to the unstable and irregular cardiac activities, so as to synthesize ECG even during abnormal beats. Furthermore, we enhance the determinacy of AirECG, i.e., to generate high-fidelity ECG, by designing a calibration guidance mechanism to combat the inherent randomness issue of the probabilistic diffusion model. Empirical evaluation demonstrates AirECG's ability of ECG synthesis with Pearson correlation coefficient (PCC) of 0.955 for normal beats. Especially for abnormal beats, the PCC still exhibits a strong correlation of 0.860, with 15.0%~21.1% improvement compared with state-of-the-art approaches.

Funder

Beijing Natural Science Foundation

Youth Top Talent Support Program

Innovation Research Group Project of NSFC

Publisher

Association for Computing Machinery (ACM)

Reference49 articles.

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3. 2024. Cardiovascular diseases. https://www.who.int/health-topics/cardiovascular-diseases/. Accessed Jan 15, 2024.

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