Arrhythmia Disease Diagnosis Based on ECG Time–Frequency Domain Fusion and Convolutional Neural Network
Electrocardiogram (ECG) signals are often used to diagnose cardiac status. However, most of the existing ECG diagnostic methods only use the time-domain information, resulting in some obviously lesion information in frequency-domain of ECG signals are not being fully utilized. Therefore, we propose...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
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| Online Access: | https://ieeexplore.ieee.org/document/10002348/ |
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| author | Bocheng Wang Guorong Chen Lu Rong Yuchuan Liu Anning Yu Xiaohui He Tingting Wen Yixuan Zhang Biaobiao Hu |
| author_facet | Bocheng Wang Guorong Chen Lu Rong Yuchuan Liu Anning Yu Xiaohui He Tingting Wen Yixuan Zhang Biaobiao Hu |
| author_sort | Bocheng Wang |
| collection | DOAJ |
| description | Electrocardiogram (ECG) signals are often used to diagnose cardiac status. However, most of the existing ECG diagnostic methods only use the time-domain information, resulting in some obviously lesion information in frequency-domain of ECG signals are not being fully utilized. Therefore, we propose a method to fuse the time and frequency domain information in ECG signals by convolutional neural network (CNN). First, we adapt multi-scale wavelet decomposition to filter the ECG signal; Then, R-wave localization is used to segment each individual heartbeat cycle; And then, the frequency domain information of this heartbeat cycle is extracted via fast Fourier transform. Finally, the temporal information is spliced with the frequency domain information and input to the neural network for classification. The experimental results show that the proposed method has the highest recognition accuracy (99.43%) of ECG singles compared with state-of-the-art methods. Clinical and Translational Impact Statement— The proposed ECG classification method provides an effective solution for ECG interrogation to quickly diagnose the presence of arrhythmia in a patient from the ECG signal. It can increase the efficiency of the interrogating physician by aiding diagnosis. |
| format | Article |
| id | doaj-art-77f8dba0ffa24a1688e488f1a4386f57 |
| institution | OA Journals |
| issn | 2168-2372 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj-art-77f8dba0ffa24a1688e488f1a4386f572025-08-20T01:52:10ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722023-01-011111612510.1109/JTEHM.2022.323279110002348Arrhythmia Disease Diagnosis Based on ECG Time–Frequency Domain Fusion and Convolutional Neural NetworkBocheng Wang0https://orcid.org/0000-0003-3757-6452Guorong Chen1https://orcid.org/0000-0003-2553-014XLu Rong2Yuchuan Liu3https://orcid.org/0000-0003-3010-7653Anning Yu4https://orcid.org/0000-0003-2017-2355Xiaohui He5Tingting Wen6Yixuan Zhang7Biaobiao Hu8School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, ChinaSchool of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, ChinaSchool of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, ChinaSchool of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, ChinaSchool of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, ChinaChongqing Vocational Institute of Engineering, Chongqing, ChinaSchool of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, ChinaSchool of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, ChinaSchool of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, ChinaElectrocardiogram (ECG) signals are often used to diagnose cardiac status. However, most of the existing ECG diagnostic methods only use the time-domain information, resulting in some obviously lesion information in frequency-domain of ECG signals are not being fully utilized. Therefore, we propose a method to fuse the time and frequency domain information in ECG signals by convolutional neural network (CNN). First, we adapt multi-scale wavelet decomposition to filter the ECG signal; Then, R-wave localization is used to segment each individual heartbeat cycle; And then, the frequency domain information of this heartbeat cycle is extracted via fast Fourier transform. Finally, the temporal information is spliced with the frequency domain information and input to the neural network for classification. The experimental results show that the proposed method has the highest recognition accuracy (99.43%) of ECG singles compared with state-of-the-art methods. Clinical and Translational Impact Statement— The proposed ECG classification method provides an effective solution for ECG interrogation to quickly diagnose the presence of arrhythmia in a patient from the ECG signal. It can increase the efficiency of the interrogating physician by aiding diagnosis.https://ieeexplore.ieee.org/document/10002348/Time–frequency domain fusionconvolutional neural networksECG diagnosis |
| spellingShingle | Bocheng Wang Guorong Chen Lu Rong Yuchuan Liu Anning Yu Xiaohui He Tingting Wen Yixuan Zhang Biaobiao Hu Arrhythmia Disease Diagnosis Based on ECG Time–Frequency Domain Fusion and Convolutional Neural Network IEEE Journal of Translational Engineering in Health and Medicine Time–frequency domain fusion convolutional neural networks ECG diagnosis |
| title | Arrhythmia Disease Diagnosis Based on ECG Time–Frequency Domain Fusion and Convolutional Neural Network |
| title_full | Arrhythmia Disease Diagnosis Based on ECG Time–Frequency Domain Fusion and Convolutional Neural Network |
| title_fullStr | Arrhythmia Disease Diagnosis Based on ECG Time–Frequency Domain Fusion and Convolutional Neural Network |
| title_full_unstemmed | Arrhythmia Disease Diagnosis Based on ECG Time–Frequency Domain Fusion and Convolutional Neural Network |
| title_short | Arrhythmia Disease Diagnosis Based on ECG Time–Frequency Domain Fusion and Convolutional Neural Network |
| title_sort | arrhythmia disease diagnosis based on ecg time x2013 frequency domain fusion and convolutional neural network |
| topic | Time–frequency domain fusion convolutional neural networks ECG diagnosis |
| url | https://ieeexplore.ieee.org/document/10002348/ |
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