Deep attention network architecture for arrhythmia classification.

Deep attention network architecture for arrhythmia classification.

<p>Deep attention network architecture for arrhythmia classification.</p>

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Bibliographic Details
Main Author:	Hang Zhao (143592) (author)
Other Authors:	Xiongfei Yin (21368130) (author)
Published:	2025
Subjects:	Physiology Biotechnology Ecology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified variational mode decomposition signal processing fields pareto optimal front ablation study evaluated 200 representative points shown significant promise ecg signal processing crayfish optimization algorithm bih arrhythmia database finite element model simulate cardiac electrophysiology div >< p ecg signal classification deep attention modules attention network based deep attention model proposed deep vmd generated using mocoa attention network cardiac electrophysiology deep model arrhythmia classification proposed model deep vmd attention modeling significant anomalies lstm modules ecg signals ecg data bayesian optimization model based increasingly based classification strategy arrhythmia characterized world mit vmd achieves vmd ), two types spectral kurtosis recent research often neglect mocoa ). mathematical foundations kl divergence key parameters k </ human heart highest accuracy driven approaches dominated sorting
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