Diagnosis of Epileptic Seizures in TLE Patients by Improved Cepstrum Analysis-Reference-Cited by-同舟云学术

Diagnosis of Epileptic Seizures in TLE Patients by Improved Cepstrum Analysis

Published:2023-12-27 Issue: Volume:8 Page:1-5
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Container-title:2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)
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Author:

Tajadini Bahar¹,Attar Hani²,Nejad Saeid Reza Seyd³,Rezakhani Soheila⁴,Boostani Reza⁵

Affiliation:

1. Shiraz University,Electrical and Computer Engineering Faculty,CSE & IT Dept., CSE&IT Dept.,Shiraz,Iran

2. Zarqa University,Faculty of Engineering,Zarqa,Jordan

3. Shahid Bahonar University,Electrical Engineering Dept., Electrical Engineering Dept.,Kerman,Iran

4. Neuroscience Research Center, School of Medicine, Medical Sciences University,Neurology Dept., Neurology Dept.,Kerman,Iran

5. School of Engineering, Shiraz University,Electrical and Computer Engineering Faculty,CSE & IT Dept., CSE&IT Dept.,Shiraz,Iran

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10590066/10590003/10590182.pdf?arnumber=10590182

Reference26 articles.

1. Seizure source localization using a hybrid second order blind identification and extended rival penalized competitive learning algorithm

2. An efficient adaptive segmentation algorithm on eeg signals to discriminate between subject with epilepsy and normal control;Kazemifar;International Review on Computers and Software (IRECOS),2013

3. Use of Time-Frequency Transforms and Kernel PCA to Classify Epileptic Patients from Control Subjects

4. Automatic seizure detection based on Teager Energy Cepstrum and pattern recognition neural networks

5. An EEG based hierarchical classification strategy to differentiate five intensities of pain