Posfay, Peter;
Kurbucz, Marcell T;
Kovacs, Peter;
Jakovac, Antal;
(2025)
Lightweight ECG signal classification via linear law-based feature extraction.
Machine Learning: Science and Technology
, 6
(3)
, Article 035001. 10.1088/2632-2153/ade6c3.
(In press).
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Abstract
This paper introduces LLT-ECG, a novel semi-supervised method for electrocardiogram (ECG) signal classification that leverages principles from theoretical physics to generate features without relying on backpropagation or hyperparameter tuning. The method identifies linear laws that capture shared patterns within a reference class, enabling compact and verifiable representations of time series data. We evaluate the method on two PhysioNet datasets, TwoLeadECG and variable projection networks (VPNet). On TwoLeadECG, a minimal configuration—using only the linear law-based transformation (LLT) and a linear decision rule—reaches 73.1% accuracy using just two features. On VPNet, LLT-ECG combined with classifiers like k-nearest neighbors and support vector machines achieves up to 96.4% accuracy, comparable to deep learning models. These results highlight LLT-ECG’s promise for lightweight, interpretable, and high-performing ECG classification.
| Type: | Article |
|---|---|
| Title: | Lightweight ECG signal classification via linear law-based feature extraction |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/2632-2153/ade6c3 |
| Publisher version: | https://iopscience.iop.org/article/10.1088/2632-21... |
| Language: | English |
| Additional information: | Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
| Keywords: | Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Interdisciplinary Applications, Multidisciplinary Sciences, Computer Science, Science & Technology - Other Topics, ECG classification, linear law, representation learning, anomaly detection, machine learning, TIME, KERNEL |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > UCL Institute for Global Prosperity |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10211257 |
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