Rebalancing Techniques for Asynchronously Distributed EEG Data to Improve Automatic Seizure Type Classification-Reference-Cited by-同舟云学术

Rebalancing Techniques for Asynchronously Distributed EEG Data to Improve Automatic Seizure Type Classification

Published:2023-03-22 Issue: Volume: Page:
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Container-title:2023 57th Annual Conference on Information Sciences and Systems (CISS)
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Author:

McCallan Niamh¹,Davidson Scot¹,Ng Kok Yew¹,Biglarbeigi Pardis²,Finlay Dewar¹,Lan Boon Leong³,McLaughlin James¹

Affiliation:

1. NIBEC School of Engineering, Ulster University, Belfast, BT15 IED,UK

2. School of Science and Engineering, University of Dundee,DD1 4HN,UK

3. Monash University,Department of ECSE and AEP,Bandar Sunway,Malaysia,47500

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10089615/10089616/10089669.pdf?arnumber=10089669

Reference27 articles.

1. Seizure Type Detection in Epileptic EEG Signal using Empirical Mode Decom-position and Support Vector Machine;wijayanto;2019 International Seminar on Intelligent Technology and Its Applications (ISITIA),0

2. A combination of statistical parameters for epileptic seizure detection and classification using VMD and NLTWSVM

3. Epileptic Seizure Classification Using Combined Labels and a Genetic Algorithm

4. Comparison of WPD, DWT and DTCWT for Multi-Class Seizure Type Classification

5. Seizure prediction — ready for a new era

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