The DOI matches its Crossref record; 107 of the 107 references it cites were found in a registry or an index.
A review on deep learning methods for ECG arrhythmia classification
Open accessAbstract
Deep Learning (DL) has recently become a topic of study in different applications including healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a vital role in patient monitoring . This paper presents a comprehensive review study on the recent DL methods applied to the ECG signal for the classification purposes. This study considers various types of the DL methods such as Convolutional Neural Network (CNN), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). From the 75 studies reported within 2017 and 2018, CNN is dominantly observed as the suitable technique for feature extraction, seen in 52% of the studies. DL methods showed high accuracy in correct classification of Atrial Fibrillation (AF) (100%), Supraventricular Ectopic Beats (SVEB) (99.8%), and Ventricular Ectopic Beats (VEB) (99.7%) using the GRU/LSTM, CNN, and LSTM, respectively.
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Cited by
Ansari, Y., Mourad, O., Qaraqe, K., & Serpedin, E. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1246746 · Cited by 291
Cross-modal multiscale multi-instance learning for long-term ECG classification
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Lee, Y., Rhee, H., Kim, G., Cheong, W. H., Kim, D. H., Song, H., Kay, S. N., Lee, J., & Kim, K. M. (2025) · Nature Communications · doi:10.1038/s41467-025-59589-2 · Cited by 19
Metadata
- Authors
- Ebrahimi, Z., Loni, M., Daneshtalab, M., & Gharehbaghi, A.
- Venue
- Expert Systems with Applications: X
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 7
- Pages
- 100033
- ISSN
- 2590-1885 (ISSN-L)
- OpenAlex
- W3035916338
- Cited by
- 447
- References
- 107
- FWCI
- 34.50
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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