A Study on Seizure Detection Performance in an Automated Process by Extracting Entropy Features-Reference-Cited by-同舟云学术

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A Study on Seizure Detection Performance in an Automated Process by Extracting Entropy Features

Published:2022-12-07 Issue: Volume: Page:
ISSN:
Container-title:2022 5th International Conference on Signal Processing and Information Security (ICSPIS)
language:
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Author:

Parui Sricheta¹,Samanta Debasis²,Chakravorty Nishant³,Mansoor Wathiq⁴,Ghosh Uttam⁵

Affiliation:

1. Indian Institute of Technology,Advanced Technology Development Centre,Kharagpur,India

2. Indian Institute of Technology,Department of Computer Science and Engineering,Kharagpur,India

3. Indian Institute of Technology,School of Medical Science and Technology,Kharagpur,India

4. University of Dubai,Dubai,United Arab Emirates

5. Meharry School of Applied Computational Sciences,Nashville,TN,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10002418/10002384/10002385.pdf?arnumber=10002385

Reference18 articles.

1. Channel selection and classification of electroencephalogram signals: An artificial neural network and genetic algorithm-based approach

2. Support Vector Channel Selection in BCI

3. A particle swarm algorithm optimization‐based SVM–KNN algorithm for epileptic EEG recognition

4. Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier

5. A statistical framework for EEG channel selection and seizure prediction on mobile

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