Myocardial Ischemia Detection Using a Reduced Number of ECG Leads

Abstract

The study is devoted to the investigation of the electrocardiographic (ECG) features to distinguish norm and myocardial ischemia in reduced set of electrocardiographic leads. In particular, for myocardial ischemia detection the spectral features of the electrocardiographic signal and characteristics of the shape of ECG waves are considered. The main features commonly used for myocardial ischemia detection are described in the paper, as well as more reliable analogs are proposed for the considered task. The approach for ECG signal preprocessing, identification of the necessary signal segments and subsequent calculation of features is described in detail. The considered features are based on the areas under the characteristic waves of the ECG signal and the spectral distribution of these waves. The most informative features for myocardial ischemia detection are identified and selected from the initial set of parameters which led to a two-fold reduction in number of ECG leads comparing to the standard 12-lead electrocardiogram. The techniques for determining the proposed features, namely the ratio of the area under T wave to the area under the P wave, as well as the ratio of the area under T wave to the area of the entire cardiac cycle, are considered. These features together with other calculated parameters are assumed to describe the majority of pathology cases and gave a high accuracy of the classification ECG to norm and ischemic myocardial diseasesince they reflect the bioelectrical processes that occur in the presence of myocardial ischemia and manifest themselves on the surface ECG. Based on the analysis of principal components and the method t-distributed stochastic neighbor embedding, the distribution of data in the space of features that characterize the classes of norm and pathology was shown. Raw ECG data in norm and with cases of myocardial ischemia were obtained from the ''PTB Diagnostic ECG Database'' used in ''The PhysioNet/Computing in Cardiology Challenge 2020''. This database contains 22353 ECG records from 290 persons with 12 ECG leads (I, II, III, aVR, aVL, aVF, and V1–V6). The database contains the high-resolution ECG signals, which enabled to obtain 10,000 cardio cycles presenting norm and myocardial ischemia pathology for the subsequent training the machine learning algorithms. Based on the obtained features, various machine learning algorithms were trained and the accuracy was compared on different combinations of ECG leads. Аs a result of cross-validation, the accuracy of myocardial ischemia detection was 99% with a standard deviation of 0.4% for 6 leads (I, II, III, AVR, AVL, AVF) and 93% with a standard deviation of 0.12% for one lead (I). Thus, it was shown, that with machine learning methods it is possible to recognize ischemic myocardial disease with high accuracy and stability using six standard ECG leads or only one ECG lead.