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This paper presents a novel method for myocardial infarction (MI) detectionusing lead II of electrocardiogram (ECG). Under our proposed method, we firstclean the noisy ECG signals using db4 wavelet, followed by an R-peak detectionalgorithm to segment the ECG signals into beats. We then translate the ECGtimeseries dataset to an equivalent dataset of gray-scale images using GramianAngular Summation Field (GASF) and Gramian Angular Difference Field (GADF)operations. Subsequently, the gray-scale images are fed into a customtwo-dimensional convolutional neural network (2D-CNN) which efficientlydifferentiates the ECG beats of the healthy subjects from the ECG beats of thesubjects with MI. We train and test the performance of our proposed method on apublic dataset, namely, Physikalisch Technische Bundesanstalt (PTB) ECG datasetfrom Physionet. Our proposed approach achieves an average classificationaccuracy of 99.68\%, 99.80\%, 99.82\%, and 99.84\% under GASF dataset withnoise and baseline wander, GADF dataset with noise and baseline wander, GASFdataset with noise and baseline wander removed, and GADF dataset with noise andbaseline wander removed, respectively. Our proposed method is able to cope withadditive noise and baseline wander, and does not require handcrafted featuresby a domain expert. Most importantly, this work opens the floor for innovationin wearable devices (e.g., smart watches, wrist bands etc.) to do accurate,real-time and early MI detection using a single-lead (lead II) ECG.

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