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.
@article{arxiv.2307.01930,
title = {Learning ECG Signal Features Without Backpropagation Using Linear Laws},
author = {Péter Pósfay and Marcell T. Kurbucz and Péter Kovács and Antal Jakovác},
journal= {arXiv preprint arXiv:2307.01930},
year = {2026}
}