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

Autor: Alali SA; Univ Rennes, Inserm, LTSI - UMR 1099, Rennes, F-35000, France., Kachenoura A; Univ Rennes, Inserm, LTSI - UMR 1099, Rennes, F-35000, France., Albera L; Univ Rennes, Inserm, LTSI - UMR 1099, Rennes, F-35000, France., Hernandez AI; Univ Rennes, Inserm, LTSI - UMR 1099, Rennes, F-35000, France., Michel C; CardiaMetrics, La Tronche, Grenoble, France., Senhadji L; Univ Rennes, Inserm, LTSI - UMR 1099, Rennes, F-35000, France., Karfoul A; Univ Rennes, Inserm, LTSI - UMR 1099, Rennes, F-35000, France. Electronic address: ahmad.karfoul@univ-rennes.fr.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Nov 26; Vol. 184, pp. 109430. Date of Electronic Publication: 2024 Nov 26.
DOI: 10.1016/j.compbiomed.2024.109430
Abstrakt: 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.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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