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Mamba-Based Prototypical Contrastive Learning With Augmented Feature Separation for Common and Rare Arrhythmia

Fengyi Guo, Ying An, Jianxin Wang

    IEEE Journal of Biomedical and Health Informatics
    |March 3, 2026
    PubMed
    Summary
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    This study introduces a Mamba-based framework for diagnosing rare arrhythmias using electrocardiograms (ECGs). The approach enhances early detection of cardiovascular conditions, even with limited data, improving patient prognosis.

    Area of Science:

    • Cardiology
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Early arrhythmia diagnosis is vital for cardiovascular health.
    • Electrocardiograms (ECGs) are standard diagnostic tools.
    • Diagnosing rare arrhythmias is challenging due to limited data.

    Purpose of the Study:

    • To develop a framework for diagnosing both common and rare arrhythmias using ECGs.
    • To address the challenge of limited data in rare disease classification.
    • To improve the accuracy of Computer-Aided Diagnosis (CAD) for arrhythmias.

    Main Methods:

    • Proposed a Mamba-based Prototypical Contrastive Learning framework (MST-PCAS).
    • Utilized a Mamba-based Spatio-Temporal Feature Fusion Network (MST) for ECG modeling.

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  • Implemented Prototypical Contrastive Learning with Augmented Feature Separation (PCAS) for enhanced classification.

    • Main Results:

    • Achieved superior rare-class recognition accuracies on PTBXL (79.13%) and Chapman (50.72%) datasets.
    • Demonstrated effectiveness in generalized Few-Shot Learning (FSL) settings.
    • Successfully identified both common and rare arrhythmia classes.

    Conclusions:

    • The MST-PCAS framework effectively diagnoses arrhythmias, including rare types.
    • This approach significantly improves rare-class recognition in ECG analysis.
    • The study offers a promising solution for challenging few-shot learning problems in medical diagnostics.