Breathing to Heartbeat: Virtual Acoustic Enhancement for Cardiac Spike Extraction via Smartphone

Abstract

Continuous monitoring of vital signs is crucial for daily health management, enabling the early detection of conditions such as premature heartbeats (HBs), atrial fibrillation, and cardiac hyperactivity. However, subtle chest movements associated with cardiac activity are often obscured by respiratory artifacts and multipath interference, presenting significant challenges in tracking minute cardiac-induced vibrations using conventional acoustic sensing. This article introduces acoustic-augmented (AcAg), a novel noncontact monitoring framework that leverages smartphone-generated acoustic signals to extract cardiac signatures from respiratory-modulated chest movements. Our key innovation lies in virtual acoustic enhancement (VAcE), a paradigm-shifting approach that mathematically reconfigures multipath propagation to transform destructive interference into constructive patterns, revealing previously obscured cardiac features. We develop a comprehensive signal processing pipeline that combines this virtual enhancement with phase-coherent analysis to capture submillimeter cardiac displacements. Furthermore, we leverage AcAgNet, a variational autoencoder that corrects signal artifacts and recovers fine-grained waveform characteristics while providing a precise heart rate (Hr) estimate. Experimental results demonstrate a mean absolute error of 0.3431 breaths per minute (bpm) across our datasets, establishing a significant advancement over state-of-the-art acoustic methods for noncontact cardiac monitoring.