In this paper, we present a novel Image Fusion Model
(IFM) for ECG heart-beat classification to overcome the
weaknesses of existing machine learning techniques that rely
either on manual feature extraction or direct utilization of 1D
raw ECG signal. At the input of IFM, we first convert the
heart-beats of ECG into three different images using Gramian
Angular Field (GAF), Recurrence Plot (RP) and Markov
Transition Field (MTF) and then fuse these images to create
a single imaging modality. We use AlexNet for feature extraction and classification and thus employ end-to-end deep
learning. We perform experiments on PhysioNet’s MIT-BIH
dataset for five different arrhythmias in accordance with the
AAMI EC57 standard and on PTB diagnostics dataset for
myocardial infarction (MI) classification. We achieved an
state-of-an-art results in terms of prediction accuracy, precision and recall.