A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals
Atrial fibrillation (AF), a common abnormal heartbeat rhythm, is a life-threatening recurrent disease that affects older adults. Automatic classification is one of the most valuable topics in medical sciences and bioinformatics, especially the detection of atrial fibrillation. However, it is difficu...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8721643/ |
| _version_ | 1828964370859687936 |
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| author | Hao Dang Muyi Sun Guanhong Zhang Xingqun Qi Xiaoguang Zhou Qing Chang |
| author_facet | Hao Dang Muyi Sun Guanhong Zhang Xingqun Qi Xiaoguang Zhou Qing Chang |
| author_sort | Hao Dang |
| collection | DOAJ |
| description | Atrial fibrillation (AF), a common abnormal heartbeat rhythm, is a life-threatening recurrent disease that affects older adults. Automatic classification is one of the most valuable topics in medical sciences and bioinformatics, especially the detection of atrial fibrillation. However, it is difficult to accurately explain the local characteristics of electrocardiogram (ECG) signals by manual analysis, due to their small amplitude and short duration, coupled with the complexity and non-linearity. Hence, in this paper, we propose a novel deep arrhythmia-diagnosis method, named deep CNN-BLSTM network model, to automatically detect the AF heartbeats using the ECG signals. The model mainly consists of four convolution layers: two BLSTM layers and two fully connected layers. The datasets of RR intervals (called set A) and heartbeat sequences (P-QRS-T waves, called set B) are fed into the above-mentioned model. Most importantly, our proposed approach achieved favorable performances with an accuracy of 99.94% and 98.63% in the training and validation set of set A, respectively. In the testing set (unseen data sets), we obtained an accuracy of 96.59%, a sensitivity of 99.93%, and a specificity of 97.03%. To the best of our knowledge, the algorithm we proposed has shown excellent results compared to many state-of-art researches, which provides a new solution for the AF automatic detection. |
| first_indexed | 2024-12-14T10:48:39Z |
| format | Article |
| id | doaj.art-2f507b5f74a049b4834864208b430579 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T10:48:39Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2f507b5f74a049b4834864208b4305792022-12-21T23:05:20ZengIEEEIEEE Access2169-35362019-01-017755777559010.1109/ACCESS.2019.29187928721643A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram SignalsHao Dang0https://orcid.org/0000-0003-1009-8200Muyi Sun1https://orcid.org/0000-0001-9506-7643Guanhong Zhang2Xingqun Qi3https://orcid.org/0000-0002-9772-5707Xiaoguang Zhou4Qing Chang5School of Automation, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Automation, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Automation, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Automation, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Automation, Beijing University of Posts and Telecommunications, Beijing, ChinaShanghai General Practice Medical Education and Research Center, Shanghai, ChinaAtrial fibrillation (AF), a common abnormal heartbeat rhythm, is a life-threatening recurrent disease that affects older adults. Automatic classification is one of the most valuable topics in medical sciences and bioinformatics, especially the detection of atrial fibrillation. However, it is difficult to accurately explain the local characteristics of electrocardiogram (ECG) signals by manual analysis, due to their small amplitude and short duration, coupled with the complexity and non-linearity. Hence, in this paper, we propose a novel deep arrhythmia-diagnosis method, named deep CNN-BLSTM network model, to automatically detect the AF heartbeats using the ECG signals. The model mainly consists of four convolution layers: two BLSTM layers and two fully connected layers. The datasets of RR intervals (called set A) and heartbeat sequences (P-QRS-T waves, called set B) are fed into the above-mentioned model. Most importantly, our proposed approach achieved favorable performances with an accuracy of 99.94% and 98.63% in the training and validation set of set A, respectively. In the testing set (unseen data sets), we obtained an accuracy of 96.59%, a sensitivity of 99.93%, and a specificity of 97.03%. To the best of our knowledge, the algorithm we proposed has shown excellent results compared to many state-of-art researches, which provides a new solution for the AF automatic detection.https://ieeexplore.ieee.org/document/8721643/Convolution neural networkdeep learningbi-directional long short-term memoryatrial fibrillation |
| spellingShingle | Hao Dang Muyi Sun Guanhong Zhang Xingqun Qi Xiaoguang Zhou Qing Chang A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals IEEE Access Convolution neural network deep learning bi-directional long short-term memory atrial fibrillation |
| title | A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals |
| title_full | A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals |
| title_fullStr | A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals |
| title_full_unstemmed | A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals |
| title_short | A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals |
| title_sort | novel deep arrhythmia diagnosis network for atrial fibrillation classification using electrocardiogram signals |
| topic | Convolution neural network deep learning bi-directional long short-term memory atrial fibrillation |
| url | https://ieeexplore.ieee.org/document/8721643/ |
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