AI RESEARCH
Efficient Neural Architectures for Real-Time ECG Interpretation on Limited Hardware
arXiv CS.LG
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ArXi:2605.09848v1 Announce Type: new Electrocardiogram (ECG) interpretation is essential for diagnosing a wide range of cardiac abnormalities. While deep learning has shown strong potential for automating ECG classification, many existing models rely on large, computationally intensive architectures that hinder practical deployment. In this paper, we present an empirical study of convolutional neural network (CNN) architectures, exploring tradeoffs between diagnostic accuracy and computational efficiency.