Classification of Cardiac Signals in Order to Diagnose Myocardial Infarction based on Extraction of Morphological Features from Spatio-Temporal Patterns of Vectorcardiogram Signals

Abstract

Background: One of the most common cardiovascular diseases (CVDs) in the world is myocardial infarction (MI). By analyzing electrocardiogram and vectorcardiography (VCG) signals, it is possible to identify and characterize heart diseases such as MI. One of the new methods of detection is the use of spatio-temporal parameters of VCG signals. This study aimed to correctly distinguish healthy signals from patients, achieve acceptable accuracy, and show the benefits of VCG and its application as a method to cover the shortcoming of electrocardiography. Methods: In this study, in addition to applying electrocardiogram signals in the time domain, spatio-temporal patterns of VCG signals were used to identify 80 patients with MI, and differentiate them from 80 healthy individuals. Findings: When combining the 12-lead electrocardiography (ECG) and the 3-lead VCG features applied to the Feedforward Neural Network classifier input, an accuracy of 91.2%, specificity of 92.6%, and specificity of 90% were obtained. The results were in higher values than when applied separately. Conclusion: The observations indicate that combined ECG and VCG methods can be effective in distinguishing MI cases from healthy cases. It is hoped that this method may be useful in the clinical evaluation and heart failure diagnosis.

Keywords

• myocardial infarction
• electrocardiography
• vectorcardiography
• wavelet transform
• neural network models