An automated classification of EEG signals based on spectrogram and CNN for epilepsy diagnosis-Reference-Cited by-同舟云学术

An automated classification of EEG signals based on spectrogram and CNN for epilepsy diagnosis

Published:2021-02-15 Issue:1 Volume:108 Page:101-110
ISSN:0925-1030
Container-title:Analog Integrated Circuits and Signal Processing
language:en
Short-container-title:Analog Integr Circ Sig Process

Author:

Mandhouj Badreddine^ORCID,Cherni Mohamed Ali,Sayadi Mounir

Publisher

Springer Science and Business Media LLC

Subject

Surfaces, Coatings and Films,Hardware and Architecture,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s10470-021-01805-2.pdf

Reference27 articles.

1. World Health Organization. « Epilepsy » [En ligne] https://www.who.int/news-room/fact- sheets/detail/epilepsy/ (Updated June 2019).

2. Sairamya, N. J., George, T., Ponraj, D. N., & Subathra, M. S. P. (2018). Automated detection of epileptic seizure using histogram of oriented gradients for analysing time frequency images of EEG signals. CCIS., 828, 932–943.

3. Vidyaratne, L. S., & Iftekharuddin, K. M. (2017). Real-time epileptic seizure detection using EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering., 25(11), 2146–2156.

4. Alotaiby, T. N., Alshebeili, S. A., Alshawi, T., et al. (2014). EEG seizure detection and prediction algorithms: A survey. EURASIP Journal on Advances Signal Processing., 2014, 183.

5. Tzallas AT, Tsipouras MG, Tsalikakis DG, Karvounis EC, AstrakasL,Konitsiotis S, et al. Automated epileptic seizure detection methods: a review study. In: Epilepsy-Histological, Electroencephalographic and Psychological Aspects. I oannia: In Tech (2012). P 75–98.

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