Design of an Integrated Myocardial Infarction Detection Model Using ECG Connectivity Features and Multivariate Time Series Classification

Abstract

Myocardial infarction (MI), commonly known as a heart attack, results from reduced blood flow to a part of the heart. Timely diagnosis of MI is very crucial due to its high mortality rate, especially among older individuals. The existing manual MI diagnosis methods using the electrocardiogram (ECG) signal necessitate the availability of qualified medical professionals while also suffering from human errors and biases. To address this, recently many methods have been proposed to automate MI diagnosis, particularly using machine learning and deep learning. However, most of these methods often employ advanced deep learning architectures like CNN or RNN directly on raw ECG data and hence require considerable computational time and power. In contrast to this, the present paper introduces an innovative MI diagnosis method wherein the multi-lead ECG signal is uniquely modeled as a multivariate time series signal to extract the multivariate sequential features of the signal. These features are then combined with the proposed novel connectivity-based features of ECG signal that exploit the relational information among ECG leads. These combined features, which uniquely encode both the sequential and relational information of the multi-lead ECG data, are then provided to a simple logistic regression classifier for classification, thus reducing the model's computational complexity and time which is extremely important in timely detection of MI. Further, the most informative ECG leads for MI detection are identified to make the model even lighter. The state-of-the-art performance of the proposed integrated model on the PTB-XL dataset verified its efficacy in the MI diagnosis.