An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model
Atrial fibrillation (AF) is a common abnormal heart rhythm disease. Therefore, the development of an AF detection system is of great significance to detect critical illnesses. In this paper, we proposed an automatic recognition method named CNN-LSTM to automatically detect the AF heartbeats based on...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/3198783 |
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| _version_ | 1832547613350559744 |
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| author | Fengying Ma Jingyao Zhang Wei Chen Wei Liang Wenjia Yang |
| author_facet | Fengying Ma Jingyao Zhang Wei Chen Wei Liang Wenjia Yang |
| author_sort | Fengying Ma |
| collection | DOAJ |
| description | Atrial fibrillation (AF) is a common abnormal heart rhythm disease. Therefore, the development of an AF detection system is of great significance to detect critical illnesses. In this paper, we proposed an automatic recognition method named CNN-LSTM to automatically detect the AF heartbeats based on deep learning. The model combines convolutional neural networks (CNN) to extract local correlation features and uses long short-term memory networks (LSTM) to capture the front-to-back dependencies of electrocardiogram (ECG) sequence data. The CNN-LSTM is feeded by processed data to automatically detect AF signals. Our study uses the MIT-BIH Atrial Fibrillation Database to verify the validity of the model. We achieved a high classification accuracy for the heartbeat data of the test set, with an overall classification accuracy rate of 97.21%, sensitivity of 97.34%, and specificity of 97.08%. The experimental results show that our model can robustly detect the onset of AF through ECG signals and achieve stable classification performance, thereby providing a suitable candidate for the automatic classification of AF. |
| format | Article |
| id | doaj-art-03fb6609fa57483a9b0f5ce668cf9a07 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-03fb6609fa57483a9b0f5ce668cf9a072025-02-03T06:43:51ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/31987833198783An Automatic System for Atrial Fibrillation by Using a CNN-LSTM ModelFengying Ma0Jingyao Zhang1Wei Chen2Wei Liang3Wenjia Yang4School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaSchool of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaSchool of Mechanical Electronic & Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaSchool of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaSchool of Mechanical Electronic & Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, ChinaAtrial fibrillation (AF) is a common abnormal heart rhythm disease. Therefore, the development of an AF detection system is of great significance to detect critical illnesses. In this paper, we proposed an automatic recognition method named CNN-LSTM to automatically detect the AF heartbeats based on deep learning. The model combines convolutional neural networks (CNN) to extract local correlation features and uses long short-term memory networks (LSTM) to capture the front-to-back dependencies of electrocardiogram (ECG) sequence data. The CNN-LSTM is feeded by processed data to automatically detect AF signals. Our study uses the MIT-BIH Atrial Fibrillation Database to verify the validity of the model. We achieved a high classification accuracy for the heartbeat data of the test set, with an overall classification accuracy rate of 97.21%, sensitivity of 97.34%, and specificity of 97.08%. The experimental results show that our model can robustly detect the onset of AF through ECG signals and achieve stable classification performance, thereby providing a suitable candidate for the automatic classification of AF.http://dx.doi.org/10.1155/2020/3198783 |
| spellingShingle | Fengying Ma Jingyao Zhang Wei Chen Wei Liang Wenjia Yang An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model Discrete Dynamics in Nature and Society |
| title | An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model |
| title_full | An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model |
| title_fullStr | An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model |
| title_full_unstemmed | An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model |
| title_short | An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model |
| title_sort | automatic system for atrial fibrillation by using a cnn lstm model |
| url | http://dx.doi.org/10.1155/2020/3198783 |
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