Inferior Myocardial Infarction Detection from lead II of ECG: A Gramian Angular Field-based 2D-CNN Approach

TL;DR

This study tackles automatic inferior MI detection from a single ECG lead (lead II) by transforming each beat into 128×128 Gramian Angular Field images (GASF and GADF) and classifying them with a custom 2D-CNN. The pipeline denoises signals, segments beats around R-peaks, and preserves temporal structure through GAF, achieving accuracies up to $99.84\%$ on denoised data and $99.68\%$ on noisy data on the PTB dataset. It demonstrates robustness to noise and baseline wander and shows that strong MI discrimination can be achieved with a single lead, enabling wearable, real-time screening. The work contributes a practical, high-performance framework for single-lead MI detection and points to future extensions across additional leads and domain adaptation.

Abstract

This paper presents a novel method for inferior myocardial infarction (MI) detection using lead II of electrocardiogram (ECG). We evaluate our proposed method on a public dataset, namely, Physikalisch Technische Bundesanstalt (PTB) ECG dataset from Physionet. Under our proposed method, we first clean the noisy ECG signals using db4 wavelet, followed by an R-peak detection algorithm to segment the ECG signals into beats. We then translate the ECG timeseries dataset to an equivalent dataset of gray-scale images using Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF) operations. Subsequently, the gray-scale images are fed into a custom two-dimensional convolutional neural network (2D-CNN) which efficiently differentiates between a healthy subject and a subject with MI. Our proposed approach achieves an average classification accuracy of 99.68%, 99.80%, 99.82%, and 99.84% under GASF dataset with noise and baseline wander, GADF dataset with noise and baseline wander, GASF dataset with noise and baseline wander removed, and GADF dataset with noise and baseline wander removed, respectively. Most importantly, this work opens the floor for innovation in wearable devices to measure lead II ECG (e.g., by a smart watch worn on right wrist, along with a smart patch on left leg), in order to do accurate, real-time and early detection of inferior wall MI.

Figures (15)

• Figure 1: The proposed method for MI detection.
• Figure 2: Lead-II ECG signal with noise and baseline wander (healthy class).
• Figure 3: Lead-II ECG signal with noise and baseline wander (MI class).
• Figure 4: Lead-II ECG signal with noise and baseline wander removed (healthy class).
• Figure 5: Lead-II ECG signal with noise and baseline wander removed (MI class).