Atrial Fibrillation Detection With Spectral Manifolds in Low-Dimensional Latent Spaces

Abstract

Atrial fibrillation (AF) remains a significant health challenge as the number of patients increases with the aging of the population worldwide. AF detection continues to be a significant challenge, particularly in the long term, to improve the quality of life of people suffering from the disease and reduce treatment costs. In recent decades, machine learning and deep learning have provided promising results in classifying heart conditions with moderate interpretability. We hypothesized that the complexity of AF could be handled in low-dimensional latent spaces in terms of non-Gaussian manifolds and that feature selection techniques on an audio-inspired feature set could improve the detection and interpretability of the AF phenomenon. Therefore, we used long-term monitoring databases in which noise-reduction filters preprocessed the signals and extracted their RR-value sequences. From these datasets, several audio spectral characteristics were calculated and used as input vectors for different types of manifold estimators. We also used the least absolute shrinkage and selection operator (LASSO) regression, a feature selection technique. Our experiments showed that these spectral representations of AF segments can yield better-defined low-dimensional embedding manifolds and acceptable intrinsic separability of AF from sinus rhythm. Moreover, including supervised stages and LASSO regression improved the overall performance of this proposal. The performance obtained in classifying AF signals versus normal ones reaches a precision of 91.2%, a recall of 95.5%, and an accuracy of 94.5%. These results pave the way for manifold learning and low-dimensional latent spaces in AF applications, and they support the advantage of using machine learning methods in different AF problems.

Keywords

• Atrial fibrillation
• manifold learning
• latent space separability
• LASSO regression
• feature selection