Chaotic Dynamics Analysis of Magnetocardiography Signals for Early Detection of Myocardial Ischemia.

The heart exhibits inherently nonlinear and chaotic electrical dynamics, making the early detection of myocardial ischemia (MI) challenging using traditional electrocardiography (ECG) or standard magnetocardiography (MCG). In this study, we propose an engineering-oriented framework that integrates classical nonlinear dynamics with machine-learning-based analysis, termed the Magnetocardiography Chaotic Dynamics Map (MCDM), to reconstruct nonlinear phase-space trajectories from 36-channel MCG recordings and capture differences in reconstructed nonlinear dynamics associated with ischemic conditions. Morphological and quantitative analyses of the MCDM patterns reveal marked differences between healthy and ischemic subjects. Using a machine-learning classifier trained on HOG and LBP descriptors, the proposed MCDM-based model achieved an accuracy of 92.19%, a sensitivity of 88.75%, a specificity of 95.63%, an F1-score of 91.91%, and an AUC of 89.80%, demonstrating effective discriminative capability for early ischemia screening. Owing to its computational simplicity and noninvasive nature, the proposed MCDM framework represents a promising tool for scalable screening of ischemic heart disease.