Confusion matrices for single-input CNN models.

<div><p>Arrhythmia is a prevalent cardiac disorder that can lead to severe complications such as stroke and cardiac arrest. While deep learning has advanced automated ECG analysis, challenges remain in accurately classifying arrhythmias due to signal variability, data imbalance, and feat...

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Bibliographic Details
Main Author:	Bin Zheng (45359) (author)
Other Authors:	Wenbo Luo (579913) (author), Mingming Zhang (362844) (author), Huiyuan Jin (14490398) (author)
Published:	2025
Subjects:	Biotechnology Infectious Diseases Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> arrhythmia two temporal resolutions sph arrhythmia databases model &# 8217 maintain spatial integrity interpretable arrhythmia classification frequency representations derived feature representation limitations ecg signals segmented arrhythmia classification based 84 %, respectively time fourier transform prioritize critical features prevalent cardiac disorder model integrates multi scale time resolution features cardiac arrest wise summation unimodal inputs signal variability severe complications results highlight previous methods long segments f1 scores deep learning data imbalance channel dependencies challenges remain attention mechanism art approaches
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Confusion matrices for single-input CNN models.

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