ECG Heart-beat Classification Using Multimodal Image Fusion
This addresses the problem of automated ECG diagnosis for medical applications, representing an incremental improvement over existing methods.
The paper tackled ECG heart-beat classification by developing an Image Fusion Model (IFM) that converts ECG signals into three image types and fuses them, achieving state-of-the-art results in accuracy, precision, and recall on MIT-BIH and PTB datasets for arrhythmias and myocardial infarction.
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.