Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification

2026-06-10

AI Summary

This work addresses the high training cost of deep neural networks in electrocardiogram (ECG) classification and the limitations of existing data filtering methods, which struggle to distinguish between difficult samples caused by noise or ambiguity and those that are genuinely informative. The authors propose ERTS, a novel approach that leverages explanation quality as a reliability signal during training. Specifically, ERTS employs Grad-CAM to generate attention maps and computes a focus score to identify samples exhibiting coherent local patterns for gradient updates. This strategy effectively differentiates informative uncertainty from unreliable uncertainty, thereby refining confidence-based sample selection. Evaluated across three ECG datasets and multiple backbone architectures, ERTS consistently achieves significant improvements in macro F1-score while substantially reducing effective training costs.

Abstract

Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required for repeated model development and deployment. This challenge is particularly evident in electrocardiogram classification, where large datasets and long training schedules make efficiency practically important. Progressive Data Dropout reduces training cost by excluding samples from gradient updates once they are learned, but it relies on model confidence and may retain samples that are difficult due to noise or ambiguity rather than useful signal. In this work, we introduce ERTS, an explainability-based reliability training signal for efficient ECG classification. ERTS uses explanation quality during training to distinguish between informative and unreliable uncertainty. Building on progressive data selection, we compute Grad-CAM attention maps for candidate samples and derive a focus score that measures whether model predictions are supported by coherent and localised patterns. Samples with low focus are filtered out, while those with meaningful attention are prioritised for gradient updates. We evaluate ERTS across three ECG datasets and multiple backbone architectures, showing consistent improvements in macro-F1 alongside reduced effective training cost. These results suggest that explanation quality can serve as a practical signal for improving both efficiency and reliability in clinical time-series learning. Code will be released.

Problem

• ECG classification
• training efficiency
• clinical time-series
• reliability
• computational cost

Innovation

• explainability
• ECG classification
• Grad-CAM
• training efficiency
• reliability signal