Detection of normal and epileptic EEG signals using by lifting based HAAR wavelet transform and artificial neural network-Reference-Cited by-同舟云学术

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Detection of normal and epileptic EEG signals using by lifting based HAAR wavelet transform and artificial neural network

Published:2021-11-09 Issue: Volume: Page:
ISSN:0975-6809
Container-title:International Journal of System Assurance Engineering and Management
language:en
Short-container-title:Int J Syst Assur Eng Manag

Author:

S Vani^ORCID,P ChandraSekhar,Sankriti Ramanarayan,G Aparna

Publisher

Springer Science and Business Media LLC

Subject

Strategy and Management,Safety, Risk, Reliability and Quality

Link

https://link.springer.com/content/pdf/10.1007/s13198-021-01454-8.pdf

Reference15 articles.

1. Aarabi A, Wallois F, Grebe R (2006) Automated neonatal seizure detection: a multistage classification system through feature selection based on relevance and redundancy analysis. Clin Neurophysiol 117(2):328–340

2. Al-Qerem A, Kharbat F, Nashwan S, Ashraf S, Blaou K (2020) General model for best feature extraction of EEG using discrete wavelet transform wavelet family and diferential evolution 2020. Int J Distrib Sens Netw. https://doi.org/10.1177/1550147720911009

3. Bonn dataset: http://epileptologie-bonn.de/cms/upload/workgroup/lehnertz/eegdata.html

4. Cetin GD, Cetin O, Bozkurt MR (2015) The detection of normal and epileptic EEG signals using ANN methods with matlab-based GUI. Int J Comput Appl 114(12)

5. Fu K, Qu J, Chai Y, Zou T (2015) Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals. Biomed Signal Process Control 18:179–185

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