MDD2DG-IRA: Multivariate Degree Distribution to Dynamic Graph With Inter-Channel Relevance Attention Mechanism for Multi-Channel Myocardial Infarction ECG Analysis

• Published in: IEEE journal of biomedical and health informatics Vol. 29; no. 8; pp. 5503 - 5514
• Main Authors: Yang, Xiaodong, Jiang, Guangkang, Zhu, Zhengping, Wu, Dandan, He, Aijun, Wang, Jun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2025
• Subjects: Algorithms; Complex networks; Convolutional neural networks; Data mining; dynamic graph convolution; ECG; Electrocardiography; Electrocardiography - methods; Feature extraction; Humans; Male; multi-head self-attention mechanism; multivariate degree distribution; myocardial infarction; Myocardial Infarction - diagnosis; Myocardial Infarction - physiopathology; Myocardium; Neural Networks, Computer; Signal Processing, Computer-Assisted; Time series analysis; Training; Transformers; Visualization
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2025.3554309

We introduced a novel methodology Multivariate Degree Distribution to Dynamic Graph (MDD2DG) with Inter-channel Relevance Attention (IRA) mechanism to analyze multi-channel Electrocardiogram (ECG) signals and explore signal connections across different channels. Our methodology comprises three main steps. First, multi-channel cardiac signals are transformed into multi-channel visual graphs to extract crucial degree distribution features. Then, degree distributions are mapped into dynamic graphs using a neural network with an IRA mechanism. After that, critical features are extracted within dynamic graphs utilizing a Graph Convolutional Neural Networks (GCNNs), and classification is subsequently performed using a multilayer perceptron. In this model, a method of multi-scale position embedding was introduced, which significantly enhanced the processing efficiency of the model by providing a simpler yet sufficiently effective feature representation. Compared to traditional complex network methods, our approach replaces fixed formula-calculated features with dynamic graph models, resulting in improved recognition accuracy. In the experiments, we achieved an impressive 99.94% classification accuracy for distinguishing ECG signals from the five distinct locations (AMI, ASMI, ALMI, IMI and ILMI) with myocardial infarction (MI) as well as those of the healthy controls (HC). This work contributes to the analysis of complex physiological signals in the field of multi-channel ECG sequence, and provides a robust approach with promising implications for improving clinical medicine and the early detection of cardiac diseases.