Epileptic Seizure Prediction Using Effective Brain Network and SVM on EEG
Authors: 
Ling Han, Xinke SongAuthors Info & Claims
Ling Han, Xinke SongAuthors Info & ClaimsPages 138 - 144
Abstract
Epilepsy is a brain dysfunction syndrome caused by repeated sudden over discharge of brain neurons, and it has certain research value to predict epileptic seizures from the aspects of directed brain network characteristics. In this paper, the directed transfer function method is used to construct an effective brain network for the EEG signals of the patients in the “interictal” and “preictal” states, and the characteristic differences of the global efficiency and clustering coefficient of the brain network in the two states are analyzed from different frequency bands. Then machine learning is used to classify these features, and the final results show that: an effective brain network between epileptic interictal and preictal constructed by DTF method has small-world properties. The classification accuracy of SVM on the clustering coefficient and global efficiency of the brain network can reach 99.9%. Then the trained classification model is selected to monitor the EEG signals of patients in real time, and the monitoring results are analyzed by “two-step” to realize the prediction of epilepsy.
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- Epileptic Seizure Prediction Using Effective Brain Network and SVM on EEG
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Published In
May 2022
190 pages
ISBN:9781450396387
DOI:10.1145/3543377
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Published: 08 August 2022
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ICBBT 2022
ICBBT 2022: 2022 14th International Conference on Bioinformatics and Biomedical Technology
May 27 - 29, 2022
Xi'an, China
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