AI RESEARCH
Enhancing AI-Based ECG Delineation with Deep Learning Denoising Techniques
arXiv CS.LG
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ArXi:2605.03183v1 Announce Type: new 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.