The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
Abstract
GOAL: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. METHODS: An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient, an individual and simple CNN will be trained by using relatively small common and patient-specific training data, and thus, such patient-specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand-crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. RESULTS: The results over the MIT-BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopic beats. CONCLUSION: Besides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner. SIGNIFICANCE: Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset.
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Cites
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Cited by
Artificial intelligence in cardiology
Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.521 · Cited by 1,202
Convolutional neural networks in medical image understanding: a survey
Sarvamangala, D. R., & Kulkarni, R. V. (2022) · Evolutionary Intelligence · doi:10.1007/s12065-020-00540-3 · Cited by 917
A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier
Eren, L., Ince, T., & Kiranyaz, S. (2019) · Journal of Signal Processing Systems · doi:10.1007/s11265-018-1378-3 · Cited by 723
Self-citationAbdeljaber, O., Avci, O., Kiranyaz, M. S., Boashash, B., Sodano, H., & Inman, D. J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.09.069 · Cited by 521
A novel application of deep learning for single-lead ECG classification
Mathews, S. M., Kambhamettu, C., & Barner, K. E. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.013 · Cited by 322
Metadata
- Authors
- Kiranyaz, S., Ince, T., & Gabbouj, M.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2016-03
- Volume
- 63
- Issue
- 3
- Pages
- 664-675
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2291961022
- Cited by
- 1,912
- References
- 24
- FWCI
- 35.02
- Type
- journal article
- Language
- en
- Open access
- closed
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