mCardiacDx: Radar-Driven Contactless Monitoring and Diagnosis of Atrial Fibrillation

Arrhythmia is a common cardiac condition that can precipitate severe complications without timely intervention. Among them, atrial fibrillation (AF) is the most common form. While continuous monitoring is essential for timely diagnosis, conventional approaches such as electrocardiogram (ECG) 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 reflections from AF patients due to disrupted spatial stability and temporal consistency caused by underlying irregular heart contractions. In this paper, we introduce mCardiacDx, a radar-driven contactless system that accurately analyzes these complex reflections and reconstructs heart pulse waveforms (HPWs) for AF monitoring and diagnosis. The key technical contributions of our work include a novel precise target localization (PTL) technique that accurately locates heart reflections despite spatial disruptions, an encoder-decoder model (HPR-Net) that effectively transforms these reflections into HPWs, addressing temporal inconsistencies, and a final analysis module for AF monitoring and diagnosis. Our evaluation on a dataset of 48 subjects (24 healthy, 24 with AF) in a seated, normal breathing, real-world setting shows that both mCardiacDx and the PTL technique significantly outperform the state-of-the-art approach in monitoring and diagnosing AF. OBJECTIVE: To develop a contactless radar-driven system, mCardiacDx, that overcomes reflection disruption challenges in AF patients to accurately reconstruct interpretable heart pulse waveforms (HPWs) for monitoring and diagnosis. METHODS AND PROCEDURES: We introduce a PTL technique to locate heart reflections despite spatial disruptions, and an encoder-decoder model (HPR-Net) to robustly process reflections and reconstruct interpretable HPWs, addressing temporal inconsistencies. The HPWs are then processed by a final analysis module for AF monitoring and diagnosis. mCardiacDx is validated against a state-of-the-art approach (baseline) on a dataset of 48 subjects (24 healthy, 24 with AF) in a seated, normal breathing, real-world setting. This validation confirms the system's robustness and generalizability to real-world seated scenarios variations in posture and environment. RESULTS: mCardiacDx significantly outperforms the baseline in both monitoring and diagnosis. HPW fidelity (Dynamic time warping (DTW) score) for AF patients improves from 5.92 to 2.92. HR/RR interval median absolute percentage error (MedAPE) reduced (e.g., HR from 9.10 % to 2.94 %; RR interval from 8.42 % to 2.95 %). Our system achieves superior diagnostic performance with 0.93 accuracy, and 0.91 recall (sensitivity), significantly surpassing the baseline's accuracy of 0.85 and recall of 0.75, while both maintain a specificity of 0.96. CONCLUSION: mCardiacDx is a robust, non-contact system for continuous cardiac care, addressing a critical gap in real-world AF monitoring and diagnosis.