Seizure Type Detection Using EEG Signals Based on Phase Synchronization and Deep Learning-Reference-Cited by-同舟云学术

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Seizure Type Detection Using EEG Signals Based on Phase Synchronization and Deep Learning

Published:2023-10-09 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 19th International Conference on Body Sensor Networks (BSN)
language:
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Author:

Shankar Anand¹,Chakraborty Debaleena¹,Saikia Manob Jyoti²,Dandapat Samarendra³,Barma Shovan¹

Affiliation:

1. Indian Institute of Information Technology,Dept. of Electronics and Communication Engineering,Guwahati,Assam,India

2. University of North Florida,Department of Electrical Engineering,Jacksonville,FL,USA,32224

3. Indian Institute of Technology,Dept. of Electrical and Electrical Engineering,Guwahati,Assam,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10330797/10330909/10331565.pdf?arnumber=10331565

Reference18 articles.

1. MP-SeizNet: A multi-path CNN Bi-LSTM Network for seizure-type classification using EEG

2. Seizure Types Classification by Generating Input Images With in-Depth Features From Decomposed EEG Signals for Deep Learning Pipeline

3. EEG based multi-class seizure type classification using convolutional neural network and transfer learning

4. SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification

5. Discrimination of Types of Seizure Using Brain Rhythms Based on Markov Transition Field and Deep Learning

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