Lightweight ECG signal classification via linear law-based feature extraction
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 p...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/ade6c3 |
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| _version_ | 1849428783564587008 |
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| author | Péter Pósfay Marcell T Kurbucz Péter Kovács Antal Jakovác |
| author_facet | Péter Pósfay Marcell T Kurbucz Péter Kovács Antal Jakovác |
| author_sort | Péter Pósfay |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e6ba87a663be4b4697c4491c1749b934 |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-e6ba87a663be4b4697c4491c1749b9342025-08-20T03:28:34ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016303500110.1088/2632-2153/ade6c3Lightweight ECG signal classification via linear law-based feature extractionPéter Pósfay0https://orcid.org/0000-0002-6769-3302Marcell T Kurbucz1https://orcid.org/0000-0002-0121-6781Péter Kovács2https://orcid.org/0000-0002-0772-9721Antal Jakovác3https://orcid.org/0000-0002-7410-0093Department of Computational Sciences, Institute for Particle and Nuclear Physics, HUN-REN Wigner Research Centre for Physics , 29-33 Konkoly-Thege Miklós Street, H-1121 Budapest, HungaryInstitute for Global Prosperity, The Bartlett, University College London , 149 Tottenham Court Road, W1T 7NE London, United KingdomDepartment of Numerical Analysis, Eötvös Loránd University , 1/c Pázmány Péter sétány, H-1117 Budapest, HungaryDepartment of Computational Sciences, Institute for Particle and Nuclear Physics, HUN-REN Wigner Research Centre for Physics , 29-33 Konkoly-Thege Miklós Street, H-1121 Budapest, Hungary; Department of Statistics, Institute of Data Analytics and Information Systems, Corvinus University of Budapest , 8 Fövám Square, H-1093 Budapest, HungaryThis 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.https://doi.org/10.1088/2632-2153/ade6c3ECG classificationlinear lawrepresentation learninganomaly detectionmachine learning |
| spellingShingle | Péter Pósfay Marcell T Kurbucz Péter Kovács Antal Jakovác Lightweight ECG signal classification via linear law-based feature extraction Machine Learning: Science and Technology ECG classification linear law representation learning anomaly detection machine learning |
| title | Lightweight ECG signal classification via linear law-based feature extraction |
| title_full | Lightweight ECG signal classification via linear law-based feature extraction |
| title_fullStr | Lightweight ECG signal classification via linear law-based feature extraction |
| title_full_unstemmed | Lightweight ECG signal classification via linear law-based feature extraction |
| title_short | Lightweight ECG signal classification via linear law-based feature extraction |
| title_sort | lightweight ecg signal classification via linear law based feature extraction |
| topic | ECG classification linear law representation learning anomaly detection machine learning |
| url | https://doi.org/10.1088/2632-2153/ade6c3 |
| work_keys_str_mv | AT peterposfay lightweightecgsignalclassificationvialinearlawbasedfeatureextraction AT marcelltkurbucz lightweightecgsignalclassificationvialinearlawbasedfeatureextraction AT peterkovacs lightweightecgsignalclassificationvialinearlawbasedfeatureextraction AT antaljakovac lightweightecgsignalclassificationvialinearlawbasedfeatureextraction |