A Novel Arrhythmia Classification Method Based On Convolutional Neural Networks Interpretation of Electrocardiogram Images

Oliveira, Alexandre Tomazati; Nóbrega, Eurípedes Guilherme de Oliveira

doi:10.1109/icit.2019.8755177

Cited by 12 publications

(7 citation statements)

References 16 publications

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“…Huang et al [24] used a short-time Fourier transform to embed heartbeats into 2D spectrograms for their classification by a 2D-CNN model. Oliviera et al [25] used wavelet transform for 2D embedding of beats. Salem et al [26] employed spectrograms to classify ECG data as well.…”

Section: Related Workmentioning

confidence: 99%

Synthetic ECG Signal Generation using Probabilistic Diffusion Models

Adib¹,

Fernandez²,

Afghah³

et al. 2023

Preprint

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Deep learning image processing models have had remarkable success in recent years in generating high quality images. Particularly, the Improved Denoising Diffusion Probabilistic Models (DDPM) have shown superiority in image quality to the state-of-the-art generative models, which motivated us to investigate its capability in generation of the synthetic electrocardiogram (ECG) signals. In this work, synthetic ECG signals are generated by the Improved DDPM and by the Wasserstein GAN with Gradient Penalty (WGAN-GP) models and then compared. To this end, we devise a pipeline to utilize DDPM in its original 2D form. First, the 1D ECG time series data are embedded into the 2D space, for which we employed the Gramian Angular Summation/Difference Fields (GASF/GADF) as well as Markov Transition Fields (MTF) to generate three 2D matrices from each ECG time series that, which when put together, form a 3-channel 2D datum. Then 2D DDPM is used to generate 2D 3-channel synthetic ECG images. The 1D ECG signals are created by deembedding the 2D generated image files back into the 1D space. This work focuses on unconditional models and the generation of only Normal ECG signals, where the Normal class from the MIT BIH Arrhythmia dataset is used as the training phase. The quality, distribution, and the authenticity of the generated ECG signals by each model are compared. Our results show that, in the proposed pipeline, the WGAN-GP model is superior to DDPM *This work was partially supported by Open Cloud Institute (OCI) at UTSA.

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Section: Related Workmentioning

confidence: 99%

Synthetic ECG Signal Generation using Probabilistic Diffusion Models

Adib¹,

Fernandez²,

Afghah³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Multiple dense CNNs were used to capture both beat-to-beat and single-beat information for analysis. Authors in [12] converted heart-beats of ECG signals to images using wavelet transform. A six layer CNN was trained on these images for heartbeat classification.…”

Section: Related Workmentioning

confidence: 99%

ECG Heart-Beat Classification Using Multimodal Image Fusion

Ahmad

Tabassum

Guan

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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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.

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“…In [36], ECG signal is converted into spectro-temporal images that were sent as an input to multiple dense convolutional neural network to capture both beatto-beat and single-beat information for analysis. Authors in [37] transformed heartbeat time intervals of ECG signals to images using wavelet transform. These images are used to train a six layer CNN for heartbeat classification.…”

Section: B Two-dimensional Cnn Approachesmentioning

confidence: 99%

ECG Heartbeat Classification Using Multimodal Fusion

et al. 2021

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Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current machine learning techniques either depend on manually extracted features or large and complex deep learning networks which merely utilize the 1D ECG signal directly. Since intelligent multimodal fusion can perform at the state-of-the-art level with an efficient deep network, therefore, in this paper, we propose two computationally efficient multimodal fusion frameworks for ECG heart beat classification called Multimodal Image Fusion (MIF) and Multimodal Feature Fusion (MFF). At the input of these frameworks, we convert the raw ECG data into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF). In MIF, we first perform image fusion by combining three imaging modalities to create a single image modality which serves as input to the Convolutional Neural Network (CNN). In MFF, we extracted features from penultimate layer of CNNs and fused them to get unique and interdependent information necessary for better performance of classifier. These informational features are finally used to train a Support Vector Machine (SVM) classifier for ECG heart-beat classification. We demonstrate the superiority of the proposed fusion models by performing experiments on PhysioNet's MIT-BIH dataset for five distinct conditions of arrhythmias which are consistent with the AAMI EC57 protocols and on PTB diagnostics dataset for Myocardial Infarction (MI) classification. We achieved classification accuracy of 99.7% and 99.2% on arrhythmia and MI classification, respectively. Source code at https://github.com/zaamad/ECG-Heartbeat-Classification-Using-Multimodal-Fusion INDEX TERMS Convolutional neural network, deep learning, ECG, image fusion, multimodal fusion.

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A Novel Arrhythmia Classification Method Based On Convolutional Neural Networks Interpretation of Electrocardiogram Images

Cited by 12 publications

References 16 publications

Synthetic ECG Signal Generation using Probabilistic Diffusion Models

Synthetic ECG Signal Generation using Probabilistic Diffusion Models

ECG Heart-Beat Classification Using Multimodal Image Fusion

ECG Heartbeat Classification Using Multimodal Fusion

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