Arrhythmia classification algorithm based on multi-head self-attention mechanism
•ECG signal preprocessing method based on wavelet transform to reduce noise.•Linear projection layer is designed to acquire semantic features of ECG signal.•Novel position encoding is proposed to obtain time and voltage series information.•Novel arrhythmia classification algorithm based on multihead...
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| Veröffentlicht in: | Biomedical signal processing and control Jg. 79; S. 104206 |
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| Format: | Journal Article |
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01.01.2023
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| Abstract | •ECG signal preprocessing method based on wavelet transform to reduce noise.•Linear projection layer is designed to acquire semantic features of ECG signal.•Novel position encoding is proposed to obtain time and voltage series information.•Novel arrhythmia classification algorithm based on multihead self-attention mechanism.•The proposed ACA-MA outperforms other state-of-the-art methods.
Cardiovascular disease is a major illness that causes human death, especially in the elderly. Timely and accurate diagnosis of arrhythmia types is the key to early prevention and diagnosis of cardiovascular diseases. This paper proposed an arrhythmia classification algorithm based on multi-head self-attention mechanism (ACA-MA). First, an ECG signal preprocessing algorithm based on wavelet transform is put forward and implemented using db6 wavelet transform to focus on improving the data quality of ECG signals and reduce the noise of ECG signals. Second, a linear projection layer for acquiring semantic features of ECG signals is designed using the matching relationship between ECG tag and segmented ECG signals. Third, a position encoding-based spatiotemporal characterization method of ECG signal sequences is designed to integrate time series information into a matrix operation. Fourth, a multi-head self-attentive mechanism capable of capturing global contextual information is proposed to extract relationships and semantic features between ECG segments and achieve semantic association and information stitching of nonadjacent ECG signals. Finally, experimental results on the arrhythmia dataset MIT/BIH show that ACA-MA outperforms other state-of-the-art methods with an overall classification accuracy of 99.4%, a specific rate of 99.41%, and a sensitivity of 97.36%. |
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| AbstractList | •ECG signal preprocessing method based on wavelet transform to reduce noise.•Linear projection layer is designed to acquire semantic features of ECG signal.•Novel position encoding is proposed to obtain time and voltage series information.•Novel arrhythmia classification algorithm based on multihead self-attention mechanism.•The proposed ACA-MA outperforms other state-of-the-art methods.
Cardiovascular disease is a major illness that causes human death, especially in the elderly. Timely and accurate diagnosis of arrhythmia types is the key to early prevention and diagnosis of cardiovascular diseases. This paper proposed an arrhythmia classification algorithm based on multi-head self-attention mechanism (ACA-MA). First, an ECG signal preprocessing algorithm based on wavelet transform is put forward and implemented using db6 wavelet transform to focus on improving the data quality of ECG signals and reduce the noise of ECG signals. Second, a linear projection layer for acquiring semantic features of ECG signals is designed using the matching relationship between ECG tag and segmented ECG signals. Third, a position encoding-based spatiotemporal characterization method of ECG signal sequences is designed to integrate time series information into a matrix operation. Fourth, a multi-head self-attentive mechanism capable of capturing global contextual information is proposed to extract relationships and semantic features between ECG segments and achieve semantic association and information stitching of nonadjacent ECG signals. Finally, experimental results on the arrhythmia dataset MIT/BIH show that ACA-MA outperforms other state-of-the-art methods with an overall classification accuracy of 99.4%, a specific rate of 99.41%, and a sensitivity of 97.36%. |
| ArticleNumber | 104206 |
| Author | He, Ling Wang, Yue Li, Shaobo Li, Yang Yang, Guanci Liu, Dan |
| Author_xml | – sequence: 1 givenname: Yue surname: Wang fullname: Wang, Yue organization: The Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, China – sequence: 2 givenname: Guanci surname: Yang fullname: Yang, Guanci email: gcyang@gzu.edu.cn organization: The Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, China – sequence: 3 givenname: Shaobo surname: Li fullname: Li, Shaobo organization: The State Key Laboratory of Public Big Data, Guizhou University, Guiyang, China – sequence: 4 givenname: Yang surname: Li fullname: Li, Yang organization: The Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, China – sequence: 5 givenname: Ling surname: He fullname: He, Ling organization: The Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, China – sequence: 6 givenname: Dan surname: Liu fullname: Liu, Dan organization: The Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, China |
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| Keywords | Attention mechanism Arrhythmia classification Feature extraction Electrocardiogram (ECG) |
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