ECG Heart-beat Classification Using Multimodal Image Fusion
- URL: http://arxiv.org/abs/2105.13536v1
- Date: Fri, 28 May 2021 01:31:35 GMT
- Title: ECG Heart-beat Classification Using Multimodal Image Fusion
- Authors: Zeeshan Ahmad, Anika Tabassum, Naimul Khan, Ling Guan
- Abstract summary: We present a novel Image Fusion Model (IFM) for ECG heart-beat classification.
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)
- Score: 13.524306011331303
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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