Target-vs-One and Target-vs-All Classification of Epilepsy Using Deep Learning Technique
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-60218-4_9
Reference20 articles.
1. Shoeibi, A., Moridian, P., Khodatars, M., et al.: An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: methods, challenges, and future works. Comput. Biol. Med. 149, 106053 (2022). https://doi.org/10.1016/j.compbiomed.2022.106053
2. Qazi, K.I., Lam, H.K., Xiao, B., et al.: Classification of epilepsy using computational intelligence techniques. CAAI Trans Intell Technol 1, 137–149 (2016). https://doi.org/10.1016/j.trit.2016.08.001
3. Lee, H.M., Gill, R.S., Bernasconi, N., Bernasconi, A.: Machine Learning in Neuroimaging of Epilepsy, pp 879–898 (2023)
4. Zubair, M., Belykh, M.V., Naik, M.U.K., et al.: Detection of epileptic seizures from EEG signals by combining dimensionality reduction algorithms with machine learning models. IEEE Sens. J. 21, 16861–16869 (2021). https://doi.org/10.1109/JSEN.2021.3077578
5. Shiragapur, B., Dhope (Shendkar), T.S., Simunic, D., et al.: Predicting epilepsy seizures using machine learning and IoT. In: Smart Innovation of Web of Things. CRC Press, pp 63–82 (2020)
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