🤖 AI Summary
This study addresses the challenge of denoising canine electrocardiogram (ECG) signals, which are highly susceptible to complex noise sources such as respiration, electromyographic interference, and lead artifacts. Conventional denoising approaches often fail to simultaneously suppress noise and preserve diagnostically critical waveform morphologies. To overcome this limitation, the authors propose an end-to-end deep learning denoising model based on an autoencoder architecture that reconstructs clean ECG signals as a preprocessing step, thereby significantly enhancing the accuracy of downstream AI-driven waveform segmentation. The method innovatively integrates denoising with the optimization of the subsequent task, demonstrating robust performance across diverse noise conditions. It effectively retains morphological features of diagnostic value and consistently outperforms existing techniques in both signal fidelity and segmentation accuracy.
📝 Abstract
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.