Cech Complex Generation with Homotopy Equivalence Framework for Myocardial Infarction Diagnosis using Electrocardiogram Signals

TL;DR

This paper introduces a novel ECG analysis framework using persistent homology and Cech complex generation with homotopy equivalence to improve myocardial infarction detection, leveraging topological features for better classification accuracy.

Contribution

It proposes a new topological data analysis method incorporating homotopy equivalence checks for ECG signal classification, enhancing detection of cardiac anomalies.

Findings

Achieved a 2.8% mean improvement in AUC over existing methods.

Effectively classified NSR, MCI, and non-MCI using topological features.

Validated on MIT-BIH and PTB datasets.

Abstract

Early and optimal identification of cardiac anomalies, especially Myocardial infarction (MCI) can aid the individual in obtaining prompt medical attention to mitigate the severity. Electrocardiogram (ECG) is a simple non-invasive physiological signal modality, that can be used to examine the electrical activity of heart tissue. Existing methods for MCI detection mostly rely on the temporal, frequency, and spatial domain analysis of the ECG signals. These conventional techniques lack in effective identification of cardiac cycle inter-dependency during diagnosis. Hence, there is an emerging need for incorporating the underlying connectivity of the intra-sessional cardiac cycles for improved anomaly detection. This article proposes a novel framework for ECG signal analysis and classification using persistent homological features through Cech Complex generation with homotopy equivalence check, by taking the above-mentioned emerging needs into account. Homological features like persistent birth-death rates, betti curves, and persistent entropy provide transparency of the regional and cardiac cycle connectivity when combined with Machine Learning (ML) models. The proposed framework is assessed using publicly available datasets (MIT-BIH and PTB), and the performance metrics of machine learning models indicate its efficacy in classifying Normal Sinus Rhythm (NSR), MCI, and non-MCI subjects, achieving a 2.8% mean improvement in AUC (area under the ROC curve) over existing approaches.