ECG-Adapt: A Novel Framework for Robust Electrocardiogram Classification Across Diverse Populations and Recording...

Area of Science:

• Biomedical Engineering
• Machine Learning in Healthcare
• Cardiology

Background:

• Electrocardiogram (ECG) signals present significant variability, challenging the development of robust cardiac classification models.
• Performance degradation occurs when ECG models trained on one dataset are applied to another due to differing patient populations, devices, and environments.

Purpose of the Study:

• To enhance the robustness and generalizability of ECG classification models using domain adaptation techniques.
• To mitigate performance degradation when applying models across diverse ECG datasets, improving diagnostic accuracy and reliability.

Main Methods:

• Exploration and advancement of unsupervised domain adaptation (UDA) techniques for ECG classification.
• Introduction of ECG-Adapt, an integrated approach aligning features within classes and across domains without requiring clustering.
• Incorporation of weakly supervised learning to prevent discriminator overfitting to pseudo-labels, enhancing model robustness.

Main Results:

• ECG-Adapt demonstrated substantial performance gains in ECG classification tasks.
• The framework improved the average F1-score by 8% for single-lead ECG problems and 7% for 12-lead problems.
• Mitigation of performance degradation when applying models across different datasets was observed.

Conclusions:

• ECG-Adapt offers a simplified workflow and improved robustness for ECG classification compared to existing methods.
• The framework shows strong potential for real-world deployment, enhancing diagnostic accuracy and reliability in diverse clinical settings.
• This approach addresses key challenges in ECG signal variability, paving the way for more generalizable AI in cardiology.