mCardiacDx: Radar-Driven Contactless Monitoring and Diagnosis of Arrhythmia

• URL: http://arxiv.org/abs/2508.02274v1
• Date: Mon, 04 Aug 2025 10:40:56 GMT
• Title: mCardiacDx: Radar-Driven Contactless Monitoring and Diagnosis of Arrhythmia
• Authors: Arjun Kumar, Noppanat Wadlom, Jaeheon Kwak, Si-Hyuck Kang, Insik Shin,
• Abstract summary: Arrhythmia is a common cardiac condition that can precipitate severe complications without timely intervention.<n>Existing contactless monitoring, primarily designed for healthy subjects, face significant challenges when analyzing reflected signals from arrhythmia patients.<n>We introduce mCardiacDx, a radar-driven contactless system that accurately analyzes reflected signals and reconstructs heart pulse waveforms for arrhythmia monitoring and diagnosis.
• Score: 4.360597490291855
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Arrhythmia is a common cardiac condition that can precipitate severe complications without timely intervention. While continuous monitoring is essential for timely diagnosis, conventional approaches such as electrocardiogram and wearable devices are constrained by their reliance on specialized medical expertise and patient discomfort from their contact nature. Existing contactless monitoring, primarily designed for healthy subjects, face significant challenges when analyzing reflected signals from arrhythmia patients due to disrupted spatial stability and temporal consistency. In this paper, we introduce mCardiacDx, a radar-driven contactless system that accurately analyzes reflected signals and reconstructs heart pulse waveforms for arrhythmia monitoring and diagnosis. The key contributions of our work include a novel precise target localization (PTL) technique that locates reflected signals despite spatial disruptions, and an encoder-decoder model that transforms these signals into HPWs, addressing temporal inconsistencies. Our evaluation on a large dataset of healthy subjects and arrhythmia patients shows that both mCardiacDx and PTL outperform state-of-the-art approach in arrhythmia monitoring and diagnosis, also demonstrating improved performance in healthy subjects.

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