CardioPatternFormer: Pattern-Guided Attention for Interpretable ECG Classification with Transformer Architecture
Berat Kutay Uğraş, Ömer Nezih Gerek, İbrahim Talha Saygı·May 26, 2025
Summary
CardioPatternFormer, a transformer-based architecture, excels in interpretable ECG classification, focusing on pattern recognition. It comprises a Cardiac Pattern Tokenizer, Physiologically Guided Attention, Multi-Resolution Temporal Encoding, and specialized classification heads. Evaluated on the Chapman-Shaoxing dataset, it demonstrates superior performance in rhythm disorder classification, aligning with clinical diagnostic difficulty gradients. Notably, it enhances interpretability by visualizing ECG regions influencing diagnoses, bridging automated analysis and clinical reasoning. This pattern-centric approach advances ECG classification, aiming for transparent and clinically integrated cardiac signal analysis.
Introduction
Background
Overview of ECG classification challenges
Importance of interpretability in medical applications
Objective
To introduce CardioPatternFormer, a transformer-based architecture for ECG classification
Highlight its focus on pattern recognition and interpretability
Method
Cardiac Pattern Tokenizer
Description of the tokenizer's role in converting ECG signals into pattern tokens
Physiologically Guided Attention
Explanation of attention mechanism tailored for cardiac patterns
Multi-Resolution Temporal Encoding
Description of encoding techniques for capturing temporal patterns at multiple resolutions
Specialized Classification Heads
Overview of heads designed for rhythm disorder classification
Evaluation
Chapman-Shaoxing Dataset
Description of the dataset used for evaluation
Performance Metrics
Metrics used to assess CardioPatternFormer's performance
Results
Detailed results on rhythm disorder classification
Comparison with existing methods
Interpretability
Visualization of Influential ECG Regions
Explanation of how CardioPatternFormer visualizes critical ECG regions for diagnosis
Bridging Automated Analysis and Clinical Reasoning
Discussion on how the architecture enhances clinical understanding
Advancements
Pattern-Centric Approach
Explanation of the pattern-centric approach in ECG classification
Transparent and Clinically Integrated Analysis
Discussion on the architecture's potential for transparent and clinically relevant cardiac signal analysis
Conclusion
Summary of Contributions
Recap of CardioPatternFormer's innovations and performance
Future Directions
Potential areas for further research and development
Clinical Impact
Discussion on the potential impact on clinical practice
Basic info
papers
signal processing
machine learning
artificial intelligence
Advanced features
Insights
What is the core innovation of CardioPatternFormer in ECG classification compared to existing methods?
On which dataset was CardioPatternFormer evaluated, and what specific aspect of ECG classification did it demonstrate superior performance in?
What are the key components of the CardioPatternFormer architecture and their respective roles in ECG classification?
How does CardioPatternFormer enhance the interpretability of ECG classification results, and why is this important for clinical integration?