A Denoising VAE for Intracardiac Time Series in Ischemic Cardiomyopathy

TL;DR

This paper presents a Variational Autoencoder (VAE) model that effectively denoises intra-cardiac signals, outperforming traditional methods and enhancing the accuracy of cardiac diagnosis and treatment in ischemic cardiomyopathy.

Contribution

The study introduces a novel VAE-based denoising approach tailored for intra-cardiac time series, addressing complex noise patterns in clinical electrophysiology data.

Findings

VAE outperforms conventional filtering methods in denoising quality.

The model effectively handles non-linear, time-varying noise in clinical signals.

Improved signal clarity may enhance diagnosis and treatment in cardiac electrophysiology.

Abstract

In the field of cardiac electrophysiology (EP), effectively reducing noise in intra-cardiac signals is crucial for the accurate diagnosis and treatment of arrhythmias and cardiomyopathies. However, traditional noise reduction techniques fall short in addressing the diverse noise patterns from various sources, often non-linear and non-stationary, present in these signals. This work introduces a Variational Autoencoder (VAE) model, aimed at improving the quality of intra-ventricular monophasic action potential (MAP) signal recordings. By constructing representations of clean signals from a dataset of 5706 time series from 42 patients diagnosed with ischemic cardiomyopathy, our approach demonstrates superior denoising performance when compared to conventional filtering methods commonly employed in clinical settings. We assess the effectiveness of our VAE model using various metrics, indicating its superior capability to denoise signals across different noise types, including time-varying non-linear noise frequently found in clinical settings. These results reveal that VAEs can eliminate diverse sources of noise in single beats, outperforming state-of-the-art denoising techniques and potentially improving treatment efficacy in cardiac EP.