ECG Heart-beat Classification Using Multimodal Image Fusion

TL;DR

This paper introduces a novel multimodal image fusion approach for ECG heart-beat classification, converting ECG signals into fused images for improved deep learning-based diagnosis of arrhythmias and myocardial infarction.

Contribution

The paper proposes a new image fusion model that combines multiple ECG image representations for enhanced deep learning classification accuracy.

Findings

Achieved state-of-the-art accuracy, precision, and recall on PhysioNet MIT-BIH dataset.

Effective fusion of GAF, RP, and MTF images improves classification performance.

Demonstrated robustness across arrhythmia and myocardial infarction datasets.

Abstract

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.