An Efficient Strategy for the Denoising of Heart Vibration Signals Acquired from an Implantable Device

Cardiac vibration signals provide insights into the mechanical function of the heart, making them potentially useful for the diagnosis and follow-up of heart failure. These signals can be captured using implantable cardiac devices incorporating a 3D accelerometer, as recently proposed in our group, enabling continuous longitudinal monitoring. However, the quality of these signals is highly affected by the presence of noise and artefacts originating from several physiological and non-physiological sources, thereby reducing the clinical effectiveness. To address this issue, a graph-based approach to improve the quality of cardiac vibration signals captured through 3D accelerometer recordings from an implantable device located in the gastric fundus, is proposed in this paper. By harnessing the inherent repetitive nature of these heart vibration signals across recorded heart cycles, the denoising problem is reformulated as the fact of inferring a target signal matrix incorporating both the smoothness on graph and low-rank constraints. The effectiveness of the proposed approach is shown through experiments conducted on real recordings acquired from a group of pigs, both with and without heart failure. The proposed approach shows superior performance compared to traditional de noising techniques.