The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats
Open accessAbstract
Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats. Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) signal. However, it can be challenging and time-consuming to visually assess the ECG signals due to the very low amplitudes. Implementing an automated system in the clinical setting can potentially help expedite diagnosis of arrhythmia, and improve the accuracies. In this paper, we propose an automated system using a combination of convolutional neural network (CNN) and long short-term memory (LSTM) for diagnosis of normal sinus rhythm, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature beats (APB) and premature ventricular contraction (PVC) on ECG signals. The novelty of this work is that we used ECG segments of variable length from the MIT-BIT arrhythmia physio bank database. The proposed system demonstrated high classification performance in the handling of variable-length data, achieving an accuracy of 98.10%, sensitivity of 97.50% and specificity of 98.70% using ten-fold cross validation strategy. Our proposed model can aid clinicians to detect common arrhythmias accurately on routine screening ECG.
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Deep learning for brain MRI segmentation: state of the art and future directions
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9983-4 · Cited by 1,129
ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform
Martis, R. J., Acharya, U. R., & Min, L. C. (2013) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2013.01.005 · Cited by 722
Self-citationExcessive Supraventricular Ectopic Activity and Increased Risk of Atrial Fibrillation and Stroke
Binici, Z., Intzilakis, T., Nielsen, O. W., Køber, L., & Sajadieh, A. (2010) · Circulation · doi:10.1161/circulationaha.109.874982 · Cited by 379
Newly acquired left bundle-branch block: the framingham study
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Cited by
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019) · Nature Medicine · doi:10.1038/s41591-018-0268-3 · Cited by 3,148
Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders
Raghavendra, U., Acharya, U. R., & Adeli, H. (2019) · European Neurology · doi:10.1159/000504292 · Cited by 174
Self-citationHong, J., Wang, Z., Chen, W., Wang, L., Lin, P., & Qu, C. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.125814 · Cited by 166
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
Convolutional neural network and long short-term memory models for ice-jam predictions
Madaeni, F., Chokmani, K., Lhissou, R., Homayouni, S., Gauthier, Y., & Tolszczuk-Leclerc, S. (2022) · The Cryosphere · doi:10.5194/tc-16-1447-2022 · Cited by 51
Metadata
- Authors
- Oh, S. L., Ng, E. Y. K., Tan, R. S., & Acharya, U. R.
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 102
- Pages
- 278-287
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W2806806521
- Cited by
- 729
- References
- 8
- FWCI
- 56.08
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Oh, S. L., Ng, E. Y. K., Tan, R. S., & Acharya, U. R. (2018). Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2018.06.002
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