用自编码器降噪提升犬类心电图分析精度
Enhancing AI-Based ECG Delineation with Deep Learning Denoising Techniques
- 用自编码器学习从噪声中还原干净心电信号
- 在有噪和无噪心电图上均表现稳定
- 适合后续心电图波形识别任务
评估犬类心电图（ECG）具有挑战性，因为噪声可能掩盖临床相关的心脏电活动。常见干扰源包括呼吸、肌肉活动、电极接触不良和外部电气伪影。传统信号降噪技术如滤波和基于小波的方法难以抑制多样化的噪声模式，同时保持对准确心电图辨识至关重要的形态特征。我们提出一种基于自编码器的神经网络模型及训练策略，用于犬类心电图分析前的降噪预处理。该模型旨在从含噪输入中重建干净的心脏信号，从而在不损害诊断重要波形的前提下实现有效降噪。我们的方法在有噪和无噪心电图记录上均表现出色，表明其对不同信号条件具备鲁棒性，适用于下游辨识任务。
原文摘要 · Abstract (English)
Evaluating canine electrocardiograms (ECGs) is challenging due to noise that can obscure clinically relevant cardiac electrical activity. Common sources of interference include respiration, muscle activity, poor lead contact, and external electrical artifacts. Classical signal denoising techniques, such as filtering and wavelet-based methods, struggle to suppress diverse noise patterns while preserving morphological features critical for accurate ECG delineation. We propose an autoencoder-based neural network model and training strategy for ECG denoising as a preprocessing step for canine ECG analysis. The model is trained to reconstruct clean cardiac signals from noisy inputs, enabling effective noise reduction without degrading diagnostically important waveforms. Our approach demonstrates strong performance across both noisy and clean ECG recordings, indicating robustness to varying signal conditions and suitability for downstream delineation tasks.
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