Magnetocardiography-Based Ischemic Heart Disease Detection and Localization Using Machine Learning Methods

• Published in: IEEE transactions on biomedical engineering Vol. 66; no. 6; pp. 1658 - 1667
• Main Authors: Tao, Rong, Lu, Jianping, Shen, Chenxing, Xie, Xiaoming, Zhang, Shulin, Huang, Xiao, Tao, Minfang, Ma, Jian, Ma, Shixin, Zhang, Chaoxiang, Zhang, Tongxin, Tang, Fakuan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Arteries; Artificial intelligence; Biomagnetics; boosting; Cardiovascular disease; Cardiovascular diseases; Classifiers; Coronary artery; Coronary artery disease; Coronary Stenosis - diagnosis; Coronary Stenosis - physiopathology; Decision trees; Diagnosis, Computer-Assisted - methods; Disease detection; Diseases; Electrocardiography; Feature extraction; Heart - physiology; Heart - physiopathology; Heart diseases; Humans; Information theory; Ischemia; ischemic heart disease detection and localization; Learning algorithms; Localization; Machine Learning; Magnetic fields; Magnetic poles; Magnetocardiography; Magnetocardiography - methods; Male; Model accuracy; Myocardial Ischemia - diagnosis; Myocardial Ischemia - physiopathology; Signal Processing, Computer-Assisted; Stenosis; Support Vector Machine; Support vector machines; Time domain analysis
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2018.2877649

Objective: This study focused on developing a fast and accurate automatic ischemic heart disease detection/localization methodology. Methods: T wave was segmented from averaged Magnetocardiography (MCG) recordings and 164 features were subsequently extracted. These features were categorized into three groups: time domain features, frequency domain features, and information theory features. Next, we compared different machine learning classifiers including: k-nearest neighbor, decision tree, support vector machine (SVM), and XGBoost. To identify ischemia heart disease (IHD) case, we selected three classifiers with best performance and applied model ensemble to average results. All 164 features were used in this stage. To localize ischemia, we classified IHD group according to stenosis locations, including left anterior descending (LAD), left circumflex artery (LCX), and right coronary artery (RCA). For this task, we used XGBoost classifier and 18 time domain features. Results: For IHD detection, the SVM-XGBoost model achieved best results with accuracy = 94.03%, precision = 86.56%, recall = 97.78%, F-score = 92.79%, AUC = 0.98, and average precision = 0.98. For ischemia localization, XGBoost model achieved accuracy = 0.74, 0.68, and 0.65, for LAD, LCX, and RCA, respectively. Conclusion: we have developed an automatic IHD detection and localization system. We find that 1. T wave repolarization synchronicity is an important factor to distinguish IHD from normal subjects 2. Magnetic field pattern is associated with stenosis location. Significance: The proposed machine learning method provides the clinicians a fast and accurate diagnosis tool to interpret MCG data, boosting its acceptance into clinics. Furthermore, the magnetic pole characteristics revealed by the method shows to be related to ischemia location, presenting the opportunity to noninvasively locate ischemia.