The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
ECG Heartbeat Classification: A Deep Transferable Representation
Open accessAbstract
Electrocardiogram (ECG) can be reliably used as a measure to monitor the functionality of the cardiovascular system. Recently, there has been a great attention towards accurate categorization of heartbeats. While there are many commonalities between different ECG conditions, the focus of most studies has been classifying a set of conditions on a dataset annotated for that task rather than learning and employing a transferable knowledge between different tasks. In this paper, we propose a method based on deep convolutional neural networks for the classification of heartbeats which is able to accurately classify five different arrhythmias in accordance with the AAMI EC57 standard. Furthermore, we suggest a method for transferring the knowledge acquired on this task to the myocardial infarction (MI) classification task. We evaluated the proposed method on PhysionNet's MIT-BIH and PTB Diagnostics datasets. According to the results, the suggested method is able to make predictions with the average accuracies of 93.4% and 95.9% on arrhythmia classification and MI classification, respectively.
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Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Conneau, A., Kiela, D., Schwenk, H., Barrault, L., & Bordes, A. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1070 · Cited by 2,049
A deep convolutional neural network model to classify heartbeats
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adam, M., Gertych, A., & Tan, R. S. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.08.022 · Cited by 1,421
Acharya, U. R., Fujita, H., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adam, M. (2017) · Information Sciences · doi:10.1016/j.ins.2017.06.027 · Cited by 937
Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring
Kachuee, M., Kiani, M. M., Mohammadzade, H., & Shabany, M. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2580904 · Cited by 621
Self-citationECG Classification Using Wavelet Packet Entropy and Random Forests
Li, T., & Zhou, M. (2016) · Entropy · doi:10.3390/e18080285 · Cited by 478
Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction
Sharma, L. N., Tripathy, R. K., & Dandapat, S. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2405134 · Cited by 296
ECG Analysis Using Multiple Instance Learning for Myocardial Infarction Detection
Sun, L., Lu, Y., Yang, K., & Li, S. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2213597 · Cited by 241
APPLICATION OF HIGHER ORDER CUMULANT FEATURES FOR CARDIAC HEALTH DIAGNOSIS USING ECG SIGNALS
MARTIS, R. J., ACHARYA, U. R., LIM, C. M., MANDANA, K. M., RAY, A. K., & CHAKRABORTY, C. (2013) · International Journal of Neural Systems · doi:10.1142/s0129065713500147 · Cited by 188
Esmaili, A., Kachuee, M., & Shabany, M. (2017) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2745081 · Cited by 162
Self-citation
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LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices
Saadatnejad, S., Oveisi, M., & Hashemi, M. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2911367 · Cited by 406
Alarsan, F. I., & Younes, M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0244-x · Cited by 256
Ham, S., Kang, M., Jang, S., Jang, J., Choi, S., Kim, T.-W., & Wang, G. (2020) · Science Advances · doi:10.1126/sciadv.aba1178 · Cited by 184
A Review of Recurrent Neural Network-Based Methods in Computational Physiology
Mao, S., & Sejdić, E. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3145365 · Cited by 128
Electrochemiluminescent tactile visual synapse enabling in situ health monitoring
Kim, W., Lee, K., Choi, S., Park, E., Kim, G., Ha, J., Kim, Y., Jang, J., Oh, J. H., Kim, H., et al. (2025) · Nature Materials · doi:10.1038/s41563-025-02124-x · Cited by 62
Metadata
- Authors
- Kachuee, M., Fazeli, S., & Sarrafzadeh, M.
- Venue
- 2018 IEEE International Conference on Healthcare Informatics (ICHI)
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 443-444
- OpenAlex
- W2798651459
- Cited by
- 447
- References
- 12
- FWCI
- 78.51
- Type
- conference paper
- Language
- en
- Open access
- green
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- Kachuee, M., Fazeli, S., & Sarrafzadeh, M. (2018). ECG Heartbeat Classification: A Deep Transferable Representation. 2018 IEEE International Conference on Healthcare Informatics (ICHI). https://doi.org/10.1109/ichi.2018.00092
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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