Why the study?
Existing machine learning techniques for ECG heart-beat classification rely either on manual feature extraction or direct utilization of 1D raw ECG signal.
A novel deep learning-based Image Fusion Model transforming 1D ECG signals into 2D images demonstrates state-of-the-art performance for automated arrhythmia and myocardial infarction classification.
May support ECG classification advances; leaves open need for clinical validation before practice change.
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 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.
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Ahmad et al. (2021) studied this question.
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