Atrial fibrillation classification based on convolutional neural networks

Lee, Kwang-Sig; Jung, Sunghoon; Gil, Yeongjoon; Son, Ho Sung

doi:10.1186/s12911-019-0946-1

Cited by 19 publications

(16 citation statements)

References 10 publications

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“… 83 proposed a multi-scaled fusion of CNNs that employs two streams of CNNs to capture features of different scales, where the learned features were visualized and compared against linear methods; Lee et al . 77 implemented and evaluated up to 30 different CNN architectures; Mousavi et al . 78 implemented a two-channel CNN model: the first one aimed to identify where to look for the detection of AF in the ECG, while the second one to perform the actual AF detection; Mousavi et al .…”

Section: ML For Detecting Afmentioning

confidence: 99%

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

Olier

Ortega‐Martorell

Pieroni

et al. 2021

Cardiovascular Research

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There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centered on applying ML for detecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modeling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centered in aims such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g., blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.

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Section: ML For Detecting Afmentioning

confidence: 99%

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

Olier

Ortega‐Martorell

Pieroni

et al. 2021

Cardiovascular Research

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show abstract

“…Kennedy et al [ 13 ] used the random forest and K-approximation method to analyze the characteristics of RR interval. Kwang-Sig [ 14 ] compared the effects of AlexNet and ResNet in the classification of atrial fibrillation. Soliński et al [ 15 ] use deep learning and hybrid QRS detection to classify atrial fibrillation signals.…”

Section: Introductionmentioning

confidence: 99%

An ECG Signal Classification Method Based on Dilated Causal Convolution

Chen

Zhu

et al. 2021

Computational and Mathematical Methods in Medicine

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The incidence of cardiovascular disease is increasing year by year and is showing a younger trend. At the same time, existing medical resources are tight. The automatic detection of ECG signals becomes increasingly necessary. This paper proposes an automatic classification of ECG signals based on a dilated causal convolutional neural network. To solve the problem that the recurrent neural network framework network cannot be accelerated by hardware equipment, the dilated causal convolutional neural network is adopted. Given the features of the same input and output time steps of the recurrent neural network and the nondisclosure of future information, the network is constructed with fully convolutional networks and causal convolution. To reduce the network depth and prevent gradient explosion or gradient disappearance, the dilated factor is introduced into the model, and the residual blocks are introduced into the model according to the shortcut connection idea. The effectiveness of the algorithm is verified in the MIT-BIH Atrial Fibrillation Database (MIT-BIH AFDB). In the experiment of the MIT-BIH AFDB database, the classification accuracy rate is 98.65%.

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“…Considering the wide usage of paper-based ECG reports [47], there is a lack in the automatic detection of cardiac problems which require special attention.…”

Section: Introductionmentioning

confidence: 99%

Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning

Özdemir

Güren

2021

Preprint

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Background: Coronavirus disease 2019 (COVID-19) has become a pandemic since its first appearance in late 2019. Deaths caused by COVID-19 are still increasing day by day and early diagnosis has become crucial. Since current diagnostic methods have many disadvantages, new investigations are needed to improve the performance of diagnosis.Methods: A novel method is proposed to automatically diagnose COVID-19 by using Electrocardiogram (ECG) data with deep learning for the first time. Moreover, a new and effective method called hexaxial feature mapping is proposed to represent 12-lead ECG to 2D colorful images. Gray-Level Co-Occurrence Matrix (GLCM) method is used to extract features and generate hexaxial mapping images. These generated images are then fed into a new Convolutional Neural Network (CNN) architecture to diagnose COVID-19.Results: Two different classification scenarios are conducted on a publicly available paper-based ECG image dataset to reveal the diagnostic capability and performance of the proposed approach. In the first scenario, ECG data labeled as COVID-19 and No-Findings (normal) are classified to evaluate COVID-19 classification ability. According to results, the proposed approach provides encouraging COVID-19 detection performance with an accuracy of 96.20% and F1-Score of 96.30%. In the second scenario, ECG data labeled as Negative (normal, abnormal, and myocardial infarction) and Positive (COVID-19) are classified to evaluate COVID-19 diagnostic ability. The experimental results demonstrated that the proposed approach provides satisfactory COVID-19 prediction performance with an accuracy of 93.00% and F1-Score of 93.20%. Furthermore, different experimental studies are conducted to evaluate the robustness of the proposed approach.Conclusion: Automatic detection of cardiovascular changes caused by COVID-19 can be possible with a deep learning framework through ECG data. This not only proves the presence of cardiovascular changes caused by COVID-19 but also reveals that ECG can potentially be used in the diagnosis of COVID-19. We believe the proposed study may provide a crucial decision-making system for healthcare professionals.Source Code: All source codes are made publicly available at: https://github.com/mkfzdmr/COVID-19-ECG-

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Atrial fibrillation classification based on convolutional neural networks

Cited by 19 publications

References 10 publications

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

An ECG Signal Classification Method Based on Dilated Causal Convolution

Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning

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