PathFusion-Net: A Rough Path Theory-Based Deep Learning Model for ECG Arrhythmia Classification.

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Medicine
• Cardiology

Background:

• Automated electrocardiogram (ECG) analysis is crucial for diagnosing cardiac arrhythmias.
• Existing deep learning models often struggle with inter-patient variability and real-world clinical applicability.
• Integrating advanced mathematical theories can enhance feature extraction for complex time-series data.

Purpose of the Study:

• To develop and evaluate PathFusion-Net, a novel deep learning model for ECG arrhythmia classification.
• To leverage Rough Path Theory for improved spatial and temporal feature extraction from ECG signals.
• To assess the model's performance in a realistic clinical diagnostic setting using an inter-patient split paradigm.

Main Methods:

• Developed PathFusion-Net by integrating Rough Path Theory with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM).
• Employed Path Signatures and Path Development to extract multi-order spatial and temporal features from ECG data.
• Utilized an inter-patient split strategy for model training and validation to simulate clinical deployment.

Main Results:

• Achieved state-of-the-art classification accuracy: 94.7% on the MIT-BIH Arrhythmia Database and 95.1% on a private clinical dataset (AAMI four-class standard, inter-patient split).
• Demonstrated competitive precision and recall for specific arrhythmia types (e.g., Ventricular Ectopic Beats: 95.2%/87.9%, Supraventricular Ectopic Beats: 75.7%/92.3%) on the MIT-BIH dataset.
• Showcased balanced performance across clinically diverse arrhythmia categories.

Conclusions:

• PathFusion-Net offers a robust and accurate framework for automated ECG arrhythmia detection and monitoring.
• Rough Path Theory shows significant potential for enhancing time-series analysis in cardiovascular applications.
• The inter-patient split paradigm provides a more clinically relevant evaluation of arrhythmia classification models.