Abstract
Magnetocardiography is a modern method of registration of the magnetic component of electromagnetic field, generated by heart activity. Magnetocardiography results are a useful source for the diagnosis of various heart diseases and states, but their usage is still undervalued in the cardiology community. In this study, a two-stage classification by correlation analysis using a k-Nearest Neighbor (k-NN) algorithm is applied for the binary classification of myocardium current density distribution maps (CDDMs). Fourteen groups of CDDMs from patients with different heart states, healthy volunteers, sportsmen, patients with negative T-peak, patients with myocardial damage, male and female patients with microvascular disease, patients with ischemic heart disease, and patients with left ventricular hypertrophy, divided into five and three different groups depending on the degree of pathology, were compared. Selection of best metric, used in classifier and number of neighbors, was performed to define the classifier with best performance for each pair of heart states. Accuracy, specificity, sensitivity, and precision values dependent on the number of neighbors are obtained for each class. The proposed method allows to obtain a value of average accuracy equal to 96%, 70% sensitivity, 98% specificity, and 70% precision.
Highlights
Magnetocardiography (MCG) is a non-invasive and risk-free technique of measuring magnetic field generated by the electrical activity of the heart using extremely sensitive devices, such as a superconducting quantum interference device
We developed a multiclass classifier based on correlation analysis [9], with value of accuracy equal to 95%, in order to provide a tool for the complex analysis of a patient’s cardiovascular system by MCG
The aim of this study is to develop a multistage classifier by combining two methods: correlation analysis as the 1st stage, performed for multiclass classification, and k-Nearest Neighbor (k-NN) classification as the 2nd stage to make the result of previous stage more accurate
Summary
Magnetocardiography (MCG) is a non-invasive and risk-free technique of measuring magnetic field generated by the electrical activity of the heart using extremely sensitive devices, such as a superconducting quantum interference device. Magnetocardiographic mapping is performed for diagnostics of ischemic heart disease [4], Wolf-Parkinson’s-White syndrome [5], and other heart failures associated with current flow changes in heart muscles. The classification of MCG data, especially current density distribution maps (CDDMs), is on its starting point and is applied only for specific disease diagnostics. We developed a multiclass classifier based on correlation analysis [9], with value of accuracy equal to 95%, in order to provide a tool for the complex analysis of a patient’s cardiovascular system by MCG. We applied a k-NN algorithm for the binary classification of CDDMs for ischemic heart disease recognition [10]. We advanced our algorithms to reach higher classification performance
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