Explaining deep learning for ECG using time-localized clusters

• URL: http://arxiv.org/abs/2509.15198v1
• Date: Thu, 18 Sep 2025 17:52:30 GMT
• Title: Explaining deep learning for ECG using time-localized clusters
• Authors: Ahcène Boubekki, Konstantinos Patlatzoglou, Joseph Barker, Fu Siong Ng, Antônio H. Ribeiro,
• Abstract summary: We propose a novel interpretability method for convolutional neural networks applied to electrocardiogram analysis.<n>Our approach extracts time-localized clusters from the model's internal representations, segmenting the ECG according to the learned characteristics.<n>This allows us to visualize how different waveform regions contribute to the model's predictions and assess the certainty of its decisions.
• Score: 6.244063807912304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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