用变分自编码器提升心肌缺血患者心内信号降噪效果
A Denoising VAE for Intracardiac Time Series in Ischemic Cardiomyopathy
- 基于5706条心内信号构建清洁信号表示，训练VAE模型去噪
- 在多种非线性时变噪声下，降噪效果优于传统滤波方法
- 适合临床心电图处理与心律失常诊断研究者使用
在心脏电生理领域，有效降低心内信号噪声对心律失常和心肌病的准确诊断与治疗至关重要。然而，传统降噪方法难以应对来自不同源的多样化噪声，这些噪声常具非线性和非平稳特性。本文提出一种变分自编码器（VAE）模型，旨在提升心室内单相动作电位（MAP）信号记录质量。通过构建42例缺血性心肌病患者共5706条时间序列的清洁信号表示，该方法在多种噪声类型下表现优异，尤其在临床常见的时变非线性噪声中显著优于常用滤波技术。评估结果表明，该VAE模型能有效消除单搏动中的多源噪声，性能超越当前先进去噪方法，或可提升心脏电生理治疗效果。
原文摘要 · Abstract (English)
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.
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