Explaining deep learning for ECG using time-localized clusters
This addresses the challenge of interpreting deep learning models in ECG analysis for clinicians and researchers, though it appears incremental as it builds on existing interpretability methods for convolutional neural networks.
The authors tackled the problem of understanding deep learning models for ECG analysis by proposing a novel interpretability method that extracts time-localized clusters from model representations, segmenting ECG waveforms and quantifying uncertainty. This allows visualization of how different waveform regions contribute to predictions and assessment of decision certainty, enhancing trust in AI-driven diagnostics and facilitating discovery of clinically relevant patterns.
Deep learning has significantly advanced electrocardiogram (ECG) analysis, enabling automatic annotation, disease screening, and prognosis beyond traditional clinical capabilities. However, understanding these models remains a challenge, limiting interpretation and gaining knowledge from these developments. In this work, we propose a novel interpretability method for convolutional neural networks applied to ECG analysis. Our approach extracts time-localized clusters from the model's internal representations, segmenting the ECG according to the learned characteristics while quantifying the uncertainty of these representations. This allows us to visualize how different waveform regions contribute to the model's predictions and assess the certainty of its decisions. By providing a structured and interpretable view of deep learning models for ECG, our method enhances trust in AI-driven diagnostics and facilitates the discovery of clinically relevant electrophysiological patterns.