Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks

TL;DR

This paper introduces a shallow CNN model that analyzes raw ECG signals from multiple leads to accurately detect inferior myocardial infarction, demonstrating improved performance over existing methods.

Contribution

The study presents a novel shallow CNN architecture for IMI detection using raw ECG data, with a subject-oriented evaluation approach to assess generalization.

Findings

Achieved 84.54% accuracy, 85.33% sensitivity, and 84.09% specificity.

Outperformed current benchmark models in IMI detection.

Analyzed feature discriminability using geometric separability index and Euclidean distance.

Abstract

Myocardial Infarction is one of the leading causes of death worldwide. This paper presents a Convolutional Neural Network (CNN) architecture which takes raw Electrocardiography (ECG) signal from lead II, III and AVF and differentiates between inferior myocardial infarction (IMI) and healthy signals. The performance of the model is evaluated on IMI and healthy signals obtained from Physikalisch-Technische Bundesanstalt (PTB) database. A subject-oriented approach is taken to comprehend the generalization capability of the model and compared with the current state of the art. In a subject-oriented approach, the network is tested on one patient and trained on rest of the patients. Our model achieved a superior metrics scores (accuracy= 84.54%, sensitivity= 85.33% and specificity= 84.09%) when compared to the benchmark. We also analyzed the discriminating strength of the features extracted by the convolutional layers by means of geometric separability index and euclidean distance and compared it with the benchmark model.