Optimized CNN-based denoising strategy for enhancing longitudinal monitoring of heart failure.

Cardiac vibration signal analysis emerges as a remarkable tool for the diagnosis of heart conditions. Our recent study shows the feasibility of the longitudinal monitoring of chronic heart diseases, particularly heart failure, using a gastric fundus implant. However, cardiac vibration data, captured from the implant, positioned at the gastric fundus, can be highly affected by different noises and artefacts. This study introduces a novel methodology for addressing denoising challenges in the longitudinal monitoring of chronic heart diseases, using gastric fundus implants. More precisely, a novel method is designed, by repurposing pre-trained convolutional neural network models, originally designed for classification tasks, with adequately chosen convolution filters. The proposed approach efficiently tackles noise and artefacts reduction in the acquired accelerometer signals. Moreover, the integration of additional Hilbert and Homomorphic envelopes enhances the implant's ability to better segment heart sounds, namely S1 and S2. The quality assessment of this denoising strategy is performed, in the lack of ground truth, by rather evaluating its impact on a classification stage that is introduced to the proposed pipeline. Compared to standard denoising matrix factorization and tensor decomposition-based methods, results on a real 3D accelerometer dataset acquired from a set of pigs, with and without heart failure, demonstrate the efficacy of such a proposed optimized CNN-based approach with the best balance between enhancing the segmentation accuracy and preserving a maximum usable record.