The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
Abstract
An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.
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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
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Cited by
Deep learning for electroencephalogram (EEG) classification tasks: a review
Craik, A., He, Y., & Contreras-Vidal, J. L. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab0ab5 · Cited by 1,758
Deep learning-based electroencephalography analysis: a systematic review
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab260c · Cited by 1,532
Xiao, C., Choi, E., & Sun, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy068 · Cited by 792
Automated EEG-based screening of depression using deep convolutional neural network
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adeli, H., & Subha, D. P. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.012 · Cited by 650
Self-citationEpileptic Seizures Detection Using Deep Learning Techniques: A Review
Shoeibi, A., Khodatars, M., Ghassemi, N., Jafari, M., Moridian, P., Alizadehsani, R., Panahiazar, M., Khozeimeh, F., Zare, A., Hosseini-Nejad, H., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18115780 · Cited by 459
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Metadata
- Authors
- Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H.
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 100
- Pages
- 270-278
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W2759483166
- Cited by
- 1,647
- References
- 58
- FWCI
- 80.60
- Type
- journal article
- Language
- en
- Open access
- closed
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- Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H. (2018). Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2017.09.017
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