A Hybrid Model for Epileptic Seizure Prediction Using EEG Data
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50993-3_21
Reference27 articles.
1. Nasseri, M., et al.: Semi-supervised training data selection improves seizure forecasting in canines with epilepsy. Biomed. Signal Process. Control 57, 101743 (2020)
2. Le Van Quyen, M., et al.: Anticipation of epileptic seizures from standard EEG recordings. Lancet 357(9251), 183–188 (2001)
3. Robinson, P., Rennie, C., Rowe, D.: Dynamics of large-scale brain activity in normal arousal states and epileptic seizures. Phys. Rev. E 65(4), 041924 (2002)
4. Hazarika, N., Chen, J.Z., Tsoi, A.C., Sergejew, A.: Classification of EEG signals using the wavelet transform. Signal Process. 59(1), 61–72 (1997)
5. Rasekhi, J., Mollaei, M.R.K., Bandarabadi, M., Teixeira, C.A., Dourado, A.: Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods. J. Neurosci. Methods 217(1–2), 9–16 (2013)
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