CardioPatternFormer: Pattern-Guided Attention for Interpretable ECG Classification with Transformer Architecture

• URL: http://arxiv.org/abs/2505.20481v1
• Date: Mon, 26 May 2025 19:36:58 GMT
• Title: CardioPatternFormer: Pattern-Guided Attention for Interpretable ECG Classification with Transformer Architecture
• Authors: Berat Kutay Uğraş, Ömer Nezih Gerek, İbrahim Talha Saygı,
• Abstract summary: We present CardioPatternFormer, a Transformer-based model for interpretable ECG classification.<n>It employs a sophisticated attention mechanism to precisely identify and classify diverse cardiac patterns.<n>It excels at discerning subtle anomalies and distinguishing multiple co-occurring conditions.
• Score: 0.40964539027092906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate ECG interpretation is vital, yet complex cardiac data and "black-box" AI models limit clinical utility. Inspired by Transformer architectures' success in NLP for understanding sequential data, we frame ECG as the heart's unique "language" of temporal patterns. We present CardioPatternFormer, a novel Transformer-based model for interpretable ECG classification. It employs a sophisticated attention mechanism to precisely identify and classify diverse cardiac patterns, excelling at discerning subtle anomalies and distinguishing multiple co-occurring conditions. This pattern-guided attention provides clear insights by highlighting influential signal regions, effectively allowing the "heart to talk" through transparent interpretations. CardioPatternFormer demonstrates robust performance on challenging ECGs, including complex multi-pathology cases. Its interpretability via attention maps enables clinicians to understand the model's rationale, fostering trust and aiding informed diagnostic decisions. This work offers a powerful, transparent solution for advanced ECG analysis, paving the way for more reliable and clinically actionable AI in cardiology.

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