A New Approach for Epileptic Seizure Detection from EEG and ECG Signals Using Wavelet Decomposition
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-52388-5_33
Reference18 articles.
1. Dua, T., De Boer, H.M., Prilipko, L.L., Saxena, S.: Epilepsy care in the world: results of an ILAE/IBE/WHO global campaign against epilepsy survey. Epilepsia 47(7), 1225–1231 (2006)
2. Acharya, U.R., Vinitha Sree, S., Swapna, G., Martis, R.J., Suri, J.S.: Automated EEG analysis of epilepsy: a review. Knowl.-Based Syst. 45, 147–65 (2013)
3. Zeng, K., Yan, J., Wang, Y., Sik, A., Ouyang, G., Li, X.: Automatic detection of absence seizures with compressive sensing EEG. Neurocomputing 171, 497–502 (2016)
4. Li, X., Song, D., Zhang, P., Zhang, Y., Hou, Y., Bin, H.: Exploring EEG features in cross-subject emotion recognition. Front. Neurosci. 12, 162 (2018)
5. Alzami, F., Wang, D., Yu, Z., You, J., Wong, HS., Han, G.: Robust epileptic seizure classification. In: Huang, D.S., Jo, K.H. (eds.) Intelligent Computing Theories and Application. ICIC 2016, Part II. LNCS, vol. 9772, pp. 363–373. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42294-7_32
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