Revolutionary ECG Analysis: Interpretable Machine Learning with Tsetlin Machines

Summary

Transforming the landscape of ECG analysis, Tsetlin Machines introduce a revolutionary and interpretable method for identifying premature ventricular contractions, challenging the dominance of opaque neural networks.

This research introduces an innovative Tsetlin Machine (TM)-based architecture for identifying premature ventricular contractions (PVCs) in long-term electrocardiogram (ECG) signals, addressing the crucial need for transparency in medical diagnostics. Unlike conventional neural network-based models, which lack interpretability, the proposed TM architecture utilises logical AND rules to provide clear, understandable patterns that align with medical knowledge. This allows medical professionals to see the reasoning behind each diagnosis, facilitating greater trust and verification.

The study compares the performance of the TM with that of advanced convolutional neural networks (CNNs) using the MIT-BIH database, demonstrating that TMs can match the accuracy of CNNs while providing much-needed transparency. The inclusion of explanatory diagrams further showcases how TMs arrive at their conclusions, emphasising their compatibility with clinical practices and enhancing their practical utility in medical settings.

By successfully marrying accuracy with interpretability, this TM-based approach not only advances PVC identification but also sets a new standard for the deployment of machine learning technologies in healthcare, particularly in critical applications like long-term ECG analysis.

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The full paper Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identification is available from arXiv.