View a PDF of the paper titled Learning ECG Signal Features Without Backpropagation Using Linear Laws, by P’eter P’osfay and 3 other authors
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.
Submission history
From: Marcell Tamás Kurbucz [view email]
[v1]
Tue, 4 Jul 2023 21:35:49 UTC (200 KB)
[v2]
Fri, 20 Dec 2024 18:18:41 UTC (336 KB)
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