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Lightweight and Interpretable LSVM for Multi-Class ECG Arrhythmia Classification Using Multi-Database PhysioNet Signals

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Τίτλος: Lightweight and Interpretable LSVM for Multi-Class ECG Arrhythmia Classification Using Multi-Database PhysioNet Signals
Συγγραφείς: Rehman, Syed Muhammad Abdul, Khan, Nadeem Ahmad, Altaf, Muhammad Awais Bin
Πηγή: 2026 IEEE 69th International Midwest Symposium on Circuits and Systems (MWSCAS) Circuits and Systems (MWSCAS), 2026 IEEE 69th International Midwest Symposium on. :415-419 Aug, 2026
Relation: 2026 IEEE 69th International Midwest Symposium on Circuits and Systems (MWSCAS)
Βάση Δεδομένων: IEEE Xplore Digital Library
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  Data: Lightweight and Interpretable LSVM for Multi-Class ECG Arrhythmia Classification Using Multi-Database PhysioNet Signals
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        Value: 10.1109/MWSCAS67364.2026.11680721
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        PageCount: 5
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      – TitleFull: Lightweight and Interpretable LSVM for Multi-Class ECG Arrhythmia Classification Using Multi-Database PhysioNet Signals
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            NameFull: Rehman, Syed Muhammad Abdul
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            NameFull: Altaf, Muhammad Awais Bin
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              Type: published
              Y: 2026
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