The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Classification of myocardial infarction with multi-lead ECG signals and deep CNN
Abstract
Abstract Myocardial infarction (MI), commonly known as heart attack, causes irreversible damage to heart muscles and even leads to death. Rapid and accurate diagnosis of MI is critical to avoid death. Blood tests and electrocardiogram (ECG) signals are used to diagnose acute MI. However, for an increase in blood enzyme values, a certain time must pass after the attack. This time lag may delay MI diagnosis. Hence, ECG diagnosis is still very important. Manual ECG interpretation requires expertise and is prone to inter-observer variability. Therefore, computer aided diagnosis may be useful in automatic detection of MI on ECG. In this study, a deep learning model with an end-to-end structure on the standard 12-lead ECG signal for the diagnosis of MI is proposed. For this purpose, the most commonly used technique, convolutional neural network (CNN) is used. Our trained CNN model with the proposed architecture yielded impressive accuracy and sensitivity performance over 99.00% for MI diagnosis on all ECG lead signals. Thus, the proposed model has the potential to provide high performance on MI detection which can be used in wearable technologies and intensive care units.
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1 retracted
Pławiak, P., & Acharya, U. R. (2020) · Neural Computing and Applications · doi:10.1007/s00521-018-03980-2 · Cited by 248
Self-citationRetractedGoldberger, 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
Deep learning for healthcare applications based on physiological signals: A review
Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., & Acharya, U. R. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.005 · Cited by 1,026
Self-citationAcharya, 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
Self-citationArrhythmia 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
Self-citationOh, S. L., Ng, E. Y. K., Tan, R. S., & Acharya, U. R. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.06.002 · Cited by 729
Self-citationNovel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system
Pławiak, P. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.09.022 · Cited by 264
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
Acharya, U. R., Fujita, H., Sudarshan, V. K., Oh, S. L., Adam, M., Koh, J. E. W., Tan, J. H., Ghista, D. N., Martis, R. J., Chua, C. K., et al. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.01.040 · Cited by 241
Self-citationA deep convolutional neural network model for automated identification of abnormal EEG signals
Yıldırım, Ö., Baloglu, U. B., & Acharya, U. R. (2020) · Neural Computing and Applications · doi:10.1007/s00521-018-3889-z · Cited by 211
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Cited by
Li, P., Hu, Y., & Liu, Z.-P. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102474 · Cited by 116
Mohd Faizal, A. S., Thevarajah, T. M., Khor, S. M., & Chang, S.-W. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106190 · Cited by 113
Dai, H., Hwang, H.-G., & Tseng, V. S. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106035 · Cited by 81
Metadata
- Authors
- Baloglu, U. B., Talo, M., Yildirim, O., Tan, R. S., & Acharya, U. R.
- Venue
- Pattern Recognition Letters
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 122
- Pages
- 23-30
- ISSN
- 0167-8655 (ISSN-L)
- OpenAlex
- W2913789442
- Cited by
- 473
- References
- 29
- FWCI
- 40.26
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Classification of myocardial infarction with multi-lead ECG signals and deep CNN. https://app.cochat.ai/papers/10.1016/j.patrec.2019.02.016
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- Baloglu, U. B., Talo, M., Yildirim, O., Tan, R. S., & Acharya, U. R. (2019). Classification of myocardial infarction with multi-lead ECG signals and deep CNN. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2019.02.016
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
