A new design of epileptic seizure detection using hybrid heuristic-based weighted feature selection and ensemble learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s41315-022-00233-3.pdf
Reference39 articles.
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2. Abdelhameed, A.M., Daoud, H.G., Bayoumi, M.: Deep convolutional bidirectional LSTM recurrent neural network for epileptic seizure detection. In: IEEE international new circuits and systems conference (NEWCAS), pp. 139–143, 2018
3. Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100, 270–278 (2018)
4. Akyol, K.: Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection. Expert Syst. Appl. 148, 113239 (2020)
5. Atal, D.K., Mukhtiar, S.: A hybrid feature extraction and machine learning approaches for epileptic seizure detection. Multidimens. Syst. Signal Process. 31, 503–525 (2020)
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