Atrial Fibrillation Detection Based on Hilbert-Huang Transform and Deep Convolutional Neural Network

GUO Yinan; SHAO Huijie; GONG Dunwei; LI Haiquan; CHEN Li

doi:10.11999/JEIT211171

Volume 44 Issue 1

Jan. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(1): 99-106

GUO Yinan, SHAO Huijie, GONG Dunwei, LI Haiquan, CHEN Li. Atrial Fibrillation Detection Based on Hilbert-Huang Transform and Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(1): 99-106. doi: 10.11999/JEIT211171

Citation:

GUO Yinan, SHAO Huijie, GONG Dunwei, LI Haiquan, CHEN Li. Atrial Fibrillation Detection Based on Hilbert-Huang Transform and Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(1): 99-106. doi: 10.11999/JEIT211171

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Atrial Fibrillation Detection Based on Hilbert-Huang Transform and Deep Convolutional Neural Network

doi: 10.11999/JEIT211171

1.
Research Institute of Artificial Intelligence, China University of Mining and Technology, Xuzhou 221116, China
2.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
3.
Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou 221006, China

Funds: The National Natural Science Foundation of China (61973305), Fundamental Research Funds of China University of Mining and Technology (2020ZDPY0302)

Received Date: 2021-10-26
Accepted Date: 2021-12-27
Rev Recd Date: 2021-12-24

Available Online: 2022-01-04

Publish Date: 2022-01-10

Abstract

Abstract

Atrial fibrillation is a common arrhythmia and its morbidity increases with age. Thus, stroke risk and cardiogenic mortality can be significantly reduced by early atrial fibrillation detection from ElectroCardioGram (ECG). In order to improve effectively detection accuracy, a novel approach is proposed to detect atrial fibrillation based on Hilbert-Huang Transform(HHT) and deep convolutional neural network. HHT is employed to transform electrocardiogram from time domain to time-frequency domain so as to enrich the feature of original data. Following that, DenseNet is introduced to deal with the detailed graph and the best model is selected during the iteration. The optimal model obtained by the proposed method achieves 99.11% and 97.25% accuracy respectively on the Massachusetts Institute of Technology - Beth Israel Hospital(MIT-BIH) and 2017 PhysioNet Challenge atrial fibrillation databases. In addition, HHT and DenseNet are compared with other time-frequency analysis and convolutional neural networks, respectively. Compared with some existing methods, the results proved that atrial fibrillation detection by HHT and Deep Convolutional Neural Network(DCNN) obtains a high detection performance.
- ElectroCardioGram (ECG),
- Atrial fibrillation,
- Hilbert-Huang Transform (HHT),
- Deep Convolutional Neural Network (DCNN)

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References(32)

References

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