MAK-Net: A Multi-Scale Attentive Kolmogorov–Arnold Network with BiGRU for Imbalanced ECG Arrhythmia Classification

MAK-Net: A Multi-Scale Attentive Kolmogorov–Arnold Network with BiGRU for Imbalanced ECG Arrhythmia Classification

Accurate classification of electrocardiogram (ECG) signals is vital for reliable arrhythmia diagnosis and informed clinical decision-making, yet real-world datasets often suffer severe class imbalance that degrades recall and F1-score. To address these limitations, we introduce MAK-Net, a hybrid dee...

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Bibliographic Details
Main Authors: Cong Zhao, Bingwei Lai, Yongzheng Xu, Yiping Wang, Haorong Dong
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Sensors
Subjects:
ECG classification
multi-scale feature fusion
attention mechanisms
bidirectional GRU (BiGRU)
class imbalance
Online Access:https://www.mdpi.com/1424-8220/25/13/3928
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