Denoising Cardiac Acceleration Signals Using an Optimal Graph-Based Strategy

Cardiac vibration signals provide critical insights into the heart's mechanical function, making them essential for diagnosing and monitoring heart failure. An implantable cardiac device equipped with a 3D accelerometer has been recently developed in our research group to capture these signals. It enables continuous long-term monitoring of heart conditions. However, noise and artifacts can significantly degrade the quality of these signals, obscuring important cardiac events and diminishing the clinical utility of the implant. To improve signal quality, a graph-based method is introduced in this paper to denoise cardiac vibration signals captured by the 3D accelerometer which is embedded in an implantable device located in the gastric fundus. The proposed method leverages the intrinsic pseudo-periodicity of heart vibration signals across cardiac cycles. More precisely, it reformulates the denoising problem as the inference of a low-rank matrix along with target signals being assumed smooth on a graph structure which is optimally learned from the observed data. The efficacy of the proposed approach compared to standard denoising methods is confirmed using real recordings from a cohort of pigs, encompassing both those with and without heart failure.