Heartbeat classification method combining multi-branch convolutional neural networks and transformer
Summary: The detection and classification of arrhythmias are crucial steps in diagnosing cardiovascular diseases. However, current deep learning-based classification methods often fail to consider both the morpho-logical and temporal features of the electrocardiogram (ECG) simultaneously. Therefore,...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
|
| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004224005285 |
| _version_ | 1797271307546525696 |
|---|---|
| author | Feiyan Zhou Jiannan Wang |
| author_facet | Feiyan Zhou Jiannan Wang |
| author_sort | Feiyan Zhou |
| collection | DOAJ |
| description | Summary: The detection and classification of arrhythmias are crucial steps in diagnosing cardiovascular diseases. However, current deep learning-based classification methods often fail to consider both the morpho-logical and temporal features of the electrocardiogram (ECG) simultaneously. Therefore, we propose a hybrid heartbeat classification method that combines Transformer and multi branch convolutional neural networks (CNNs). Then, use the fusion module to stitch the features obtained from different classifiers. We performed three different heartbeat classification protocols on the MIT-BIH arrhythmia (MIT-BIH-AR) database and analyzed performance on SVEB and VEB classes to validate our method. The first was an intra-patient protocol with an overall accuracy of 99.5%, with 92.4% and 99.9% for Sen and Spe on SVEB and 98.2% and 99.9% for Sen and Spe on VEB. The latter two were inter-patient protocols, and we divided the training and test sets using different records, and the results showed an overall accuracy of 98.8% and 97.2%, respectively. |
| first_indexed | 2024-03-07T13:59:52Z |
| format | Article |
| id | doaj.art-30453ed60c944af6bf4cc2a273896ead |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-07T13:59:52Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-30453ed60c944af6bf4cc2a273896ead2024-03-07T05:29:02ZengElsevieriScience2589-00422024-03-01273109307Heartbeat classification method combining multi-branch convolutional neural networks and transformerFeiyan Zhou0Jiannan Wang1Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin 541004, China; Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin 541004, China; Corresponding authorKey Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin 541004, China; Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin 541004, ChinaSummary: The detection and classification of arrhythmias are crucial steps in diagnosing cardiovascular diseases. However, current deep learning-based classification methods often fail to consider both the morpho-logical and temporal features of the electrocardiogram (ECG) simultaneously. Therefore, we propose a hybrid heartbeat classification method that combines Transformer and multi branch convolutional neural networks (CNNs). Then, use the fusion module to stitch the features obtained from different classifiers. We performed three different heartbeat classification protocols on the MIT-BIH arrhythmia (MIT-BIH-AR) database and analyzed performance on SVEB and VEB classes to validate our method. The first was an intra-patient protocol with an overall accuracy of 99.5%, with 92.4% and 99.9% for Sen and Spe on SVEB and 98.2% and 99.9% for Sen and Spe on VEB. The latter two were inter-patient protocols, and we divided the training and test sets using different records, and the results showed an overall accuracy of 98.8% and 97.2%, respectively.http://www.sciencedirect.com/science/article/pii/S2589004224005285Biomedical engineeringArtificial intelligence |
| spellingShingle | Feiyan Zhou Jiannan Wang Heartbeat classification method combining multi-branch convolutional neural networks and transformer iScience Biomedical engineering Artificial intelligence |
| title | Heartbeat classification method combining multi-branch convolutional neural networks and transformer |
| title_full | Heartbeat classification method combining multi-branch convolutional neural networks and transformer |
| title_fullStr | Heartbeat classification method combining multi-branch convolutional neural networks and transformer |
| title_full_unstemmed | Heartbeat classification method combining multi-branch convolutional neural networks and transformer |
| title_short | Heartbeat classification method combining multi-branch convolutional neural networks and transformer |
| title_sort | heartbeat classification method combining multi branch convolutional neural networks and transformer |
| topic | Biomedical engineering Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2589004224005285 |
| work_keys_str_mv | AT feiyanzhou heartbeatclassificationmethodcombiningmultibranchconvolutionalneuralnetworksandtransformer AT jiannanwang heartbeatclassificationmethodcombiningmultibranchconvolutionalneuralnetworksandtransformer |