Automatic classification of seizure and seizure-free EEG signals based on phase space reconstruction features
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10867-024-09654-6.pdf
Reference36 articles.
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2. Sukriti, Chakraborty, M., Debjani, M.: Automated detection of epileptic seizures using multiscale and refined composite multiscale dispersion entropy. Chaos Solitons Fractals 146, 110939 (2021). https://doi.org/10.1016/j.chaos.2021.110939
3. Zhang, T., Wanzhong, C.: LMD based features for the automatic seizure detection of EEG signals using SVM. IEEE Trans. Neural Syst. Rehab. Eng. 25(8), 1100–1108 (2016). https://doi.org/10.1109/TNSRE.2016.2611601
4. Joshi, V., Pachori, R.B., Vijesh, A.: Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomed. Signal Process. Control 9, 1–5 (2014). https://doi.org/10.1016/j.bspc.2013.08.006
5. Ghosh, D.S., Hojjat, A., Nahid, D.: Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection. IEEE Trans. Biomed. Eng. 55(2), 512–518 (2008). https://doi.org/10.1109/TBME.2007.905490
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