Patient-specific seizure detection method using nonlinear mode decomposition for long-term EEG signals-Reference-Cited by-同舟云学术

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Patient-specific seizure detection method using nonlinear mode decomposition for long-term EEG signals

Published:2020-10-30 Issue:12 Volume:58 Page:3075-3088
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Li Mingyang,Sun Xiaoying,Chen Wanzhong

Funder

Science and Technology Project of Education Department in Jilin Province

Science and Technology Development Plan in Jilin Province

China and China Post-doctoral Innovative Talents Support Program

China Postdoctoral Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

http://link.springer.com/content/pdf/10.1007/s11517-020-02279-6.pdf

Reference41 articles.

1. Polat K, Guenes S (2008) Artificial immune recognition system with fuzzy resource allocation mechanism classifier, principal component analysis and FFT method based new hybrid automated identification system for classification of EEG signals. Expert Syst Appl 34(3):2039–2048

2. Bhardwaj A, Tiwari A, Krishna R, Varma V (2016) A novel genetic programming approach for epileptic seizure detection. Comput Methods Prog Biomed 124:2–18

3. Wang G, Deng Z, Choi KS (2017) Detection of epilepsy with electroencephalogram using rule-based classifiers. Neurocomputing 228:283–290

4. Li J, Zhou W, Yuan S, Zhang Y, Li C, Wu Q (2015) An improved sparse representation over learned dictionary method for seizure detection. Int J Neural Syst 26(1):1550035

5. Li Y, Wang XD, Luo ML, Li K, Yang X, Guo Q (2018) Epileptic seizure classification of EEGs using time-frequency analysis based multiscale radial basis functions. IEEE Journal of Biomedical and Health Informatics 22(2):386–397

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