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

2025-05-26

AI Summary

To address the clinical deployment barrier posed by the “black-box” nature of AI models in ECG classification, this paper proposes an interpretable Transformer architecture that models ECG signals as a cardiac temporal “language.” We introduce a novel pattern-guided attention mechanism enabling fine-grained waveform localization and clinically interpretable decision-making. The model integrates temporal positional encoding, multi-head pattern-aware attention, and a differentiable signal-region focusing module. Evaluated on a multicenter, complex ECG dataset encompassing multiple pathologies, it achieves 98.2% classification accuracy, while its attention heatmaps exhibit 91.4% spatial alignment with cardiologist-annotated critical waveform segments. This work overcomes key limitations of conventional black-box models in multi-pathology ECG interpretation, significantly enhancing clinical trustworthiness and human–AI collaborative diagnostic efficiency.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• Improving ECG interpretation using interpretable AI models
• Classifying diverse cardiac patterns with Transformer architecture
• Enhancing clinical trust via transparent attention-based insights

Innovation

• Transformer-based model for ECG classification
• Pattern-guided attention for interpretable insights
• Clear visualization via attention maps