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arXiv · 2505.20481

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

Berat Kutay Uğraş·Ömer Nezih Gerek·İbrahim Talha Saygı
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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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Berat Kutay Uğraş, Ömer Nezih Gerek, İbrahim Talha Saygı. 2025-05-26. CardioPatternFormer: Pattern-Guided Attention for Interpretable ECG Classification with Transformer Architecture. https://arxiv.org/abs/2505.20481

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