The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network
Open accessAbstract
Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.
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Cites
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
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The wavelet transform, time-frequency localization and signal analysis
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The impact of the MIT-BIH Arrhythmia Database
Moody, G. B., & Mark, R. G. (2001) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.932724 · Cited by 4,812
Beyond short snippets: Deep networks for video classification
Ng, J. Y.-H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., & Toderici, G. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299101 · Cited by 2,062
Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval Features
deChazal, P., O'Dwyer, M., & Reilly, R. B. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827359 · Cited by 1,671
Arrhythmia detection using deep convolutional neural network with long duration ECG signals
Yıldırım, Ö., Pławiak, P., Tan, R.-S., & Acharya, U. R. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.09.009 · Cited by 851
CNN: Single-label to Multi-label
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HCP: A Flexible CNN Framework for Multi-Label Image Classification
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A patient-adaptable ECG beat classifier using a mixture of experts approach
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Cited by
Suri, J. S., Bhagawati, M., Paul, S., Protogerou, A. D., Sfikakis, P. P., Kitas, G. D., Khanna, N. N., Ruzsa, Z., Sharma, A. M., Saxena, S., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12030722 · Cited by 55
Metadata
- Authors
- Wang, T., Lu, C., Sun, Y., Yang, M., Liu, C., & Ou, C.
- Venue
- Entropy
- Publisher
- MDPI AG
- Published
- 2021-01-18
- Volume
- 23
- Issue
- 1
- Pages
- 119
- ISSN
- 1099-4300 (ISSN-L)
- OpenAlex
- W3123326053
- Cited by
- 308
- References
- 30
- FWCI
- 31.92
- Type
- journal article
- Language
- en
- Open access
- gold
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