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arXiv · 2307.01930

Learning ECG Signal Features Without Backpropagation Using Linear Laws

Péter Pósfay·Marcell T. Kurbucz·Péter Kovács·Antal Jakovác
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Abstract

This paper introduces LLT-ECG, a novel method for electrocardiogram (ECG) signal classification that leverages concepts from theoretical physics to automatically generate features from time series data. Unlike traditional deep learning approaches, LLT-ECG operates in a forward manner, eliminating the need for backpropagation and hyperparameter tuning. By identifying linear laws that capture shared patterns within specific classes, the proposed method constructs a compact and verifiable representation, enhancing the effectiveness of downstream classifiers. We demonstrate LLT-ECG's state-of-the-art performance on real-world ECG datasets from PhysioNet, underscoring its potential for medical applications where speed and verifiability are crucial.

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BibTeXRIS

Péter Pósfay, Marcell T. Kurbucz, Péter Kovács, Antal Jakovác. 2023-07-04. Learning ECG Signal Features Without Backpropagation Using Linear Laws. https://doi.org/10.1088/2632-2153%2Fade6c3

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