Automated magnetocardiography classification using a deformable convolutional block attention module

作     者：Wang, Ruizhe Pang, Jiaojiao Han, Xiaole Xiang, Min Ning, Xiaolin 

作者机构：Beihang Univ Sch Instrumentat & Optoelect Engn Key Lab Ultraweak Magnet Field Measurement Technol Minist Educ Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Zhejiang Prov Key Lab Ultraweak Magnet Field Space Hangzhou 310051 Zhejiang Peoples R China Beihang Univ Hangzhou Inst Natl Extremely Weak Magnet Field Inf Hangzhou 310028 Zhejiang Peoples R China Shandong Univ Inst Magnet Field Free Med & Funct Imaging Shandong Key Lab Magnet Field Free Med & Funct Ima Jinan Peoples R China Shandong Univ Shandong Prov Clin Res Ctr Emergency & Crit Care M Dept Emergency Med Qilu Hosp Jinan Peoples R China Shandong Univ Natl Innovat Platform Ind Educ Intearat Med Engn I Jinan Peoples R China Hefei Natl Lab Hefei 230088 Anhui Peoples R China 

出 版 物：《BIOMEDICAL SIGNAL PROCESSING AND CONTROL》 (Biomed. Signal Process. Control)

年 卷 期：2025年第105卷

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 10[医学] 

基　　金：Innovation Program for Quantum Science and Technology [2021ZD0300500, 2022-189-181] National Natural Science Foundation of China [62101017, U23A20485] Key R&D Program of Shandong Province [2022ZLGX03] 

主　　题：Magnetocardiography Coronary artery disease Convolutional neural network Attention mechanism Deformable convolutional block attention module 

摘      要：Objective: This study developed a fast and accurate automated method for magnetocardiography (MCG) classification. Approach: We propose a deformable convolutional block attention module (DCBAM)-based method for classifying coronary artery disease (CAD) using MCG. After preprocessing, the raw MCG data were segmented into individual heartbeat segments and encoded into image representations using the Hilbert curve to convert the temporal features into spatial image features. We combined DCBAM with convolutional neural networks (CNNs) for MCG classification. DCBAM incorporated a deformable convolutional architecture along with temporal and spatial attention mechanisms to capture representative and correlative features of the image representation MCG along the temporal and spatial multichannel dimensions. We performed ablation experiments to evaluate the rationality and validity of the proposed model structure. Additionally, we performed an interpretability analysis to investigate the model s region of interest for CAD diagnosis. Results: The proposed method achieved an average accuracy of 93.57%, precision of 94.71%, sensitivity of 92.56%, specificity of 94.68%, and average F1-score of 93.60%. In contrast to existing methods, our proposed model achieved superior diagnostic classification results in MCG with fewer parameters. Significance: Integrating DCBAM with image-representation MCG establishes a novel feature extraction method that enhances the clinical utility of MCG and effectively addresses long-range dependencies and spatiotemporal inconsistencies in time-series signal analysis.