MST-DGCN: Multi-Scale Temporal–Dynamic Graph Convolutional with Orthogonal Gate for Imbalanced Multi-Label ECG Arrhythmia Classification

Multi-label arrhythmia classification from 12-lead ECG signals is a tricky problem, including spatiotemporal feature extraction, feature fusion, and class imbalance. To address these issues, a multi-scale temporal–dynamic graph convolutional with orthogonal gates method, termed MST-DGCN, is proposed...

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Bibliographic Details

Published in:	AI
Main Authors:	Jie Chen, Mingfeng Jiang, Xiaoyu He, Yang Li, Jucheng Zhang, Juan Li, Yongquan Wu, Wei Ke
Format:	Article
Language:	English
Published:	MDPI AG 2025-09-01
Subjects:	ECG multi-scale dynamic graph convolution orthogonal gated fusion multi-label arrhythmia classification
Online Access:	https://www.mdpi.com/2673-2688/6/9/219