Elastic Net Regularization and Gabor Dictionary for Classification of Heart Sound Signals using Deep Learning
In this article, we propose the optimization of the resolution of time-frequency atoms and the regularization of fitting models to obtain better representations of heart sound signals. This is done by evaluating the classification performance of deep learning (DL) networks in discriminating five heart valvular conditions based on a new class of time-frequency feature matrices derived from the fitting models. We inspect several combinations of resolution and regularization, and the optimal one is that provides the highest performance. To this end, a fitting model is obtained based on a heart sound signal and an overcomplete dictionary of Gabor atoms using elastic net regularization of line...
| Authors | Mahmoud Fakhry, Ascensión Gallardo-Antolín |
|---|---|
| Primary category | cs.SD |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.