Open Access Repository

Seizure prediction using directed transfer function and convolution neural network on intracranial EEG

Downloads

Downloads per month over past year

Wang, G, Wang, D, Du, C, Li, K, Zhang, J, Liu, Z, Tao, Y, Wang, M, Cao, Z ORCID: 0000-0003-3656-0328 and Yan, X 2020 , 'Seizure prediction using directed transfer function and convolution neural network on intracranial EEG' , IEEE Transactions on Neural Systems and Rehabilitation Engineering , pp. 1-11 , doi: 10.1109/TNSRE.2020.3035836.

[img]
Preview
PDF (Post print)
141890 - Seizur...pdf | Download (541kB)

| Preview

Abstract

Automatic seizure prediction promotes the development of closed-loop treatment system on intractable epilepsy. In this study, by considering the specific information exchange between EEG channels from the perspective of whole brain activities, the convolution neural network (CNN) and the directed transfer function (DTF) were merged to present a novel method for patient-specific seizure prediction. Firstly, the intracranial electroencephalogram (iEEG) signals were segmented and the information flow features of iEEG signals were calculated by using the DTF algorithm. Then, these features were reconstructed as the channel-frequency maps according to channel pairs and the frequency of information flow. Finally, these maps were fed into the CNN model and the outputs were post-processed by the moving average approach to predict the epileptic seizures. By the evaluation of cross-validation method, the proposed algorithm achieved the averaged sensitivity of 90.8%, the averaged false prediction rate of 0.08 per hour. Compared to the random predictor and other existing algorithms tested on the Freiburg EEG dataset, our proposed method achieved better performance for seizure prediction in all patients. These results demonstrated that the proposed algorithm could provide an robust seizure prediction solution by using deep learning to capture the brain network changes of iEEG signals from epileptic patients.

Item Type: Article
Authors/Creators:Wang, G and Wang, D and Du, C and Li, K and Zhang, J and Liu, Z and Tao, Y and Wang, M and Cao, Z and Yan, X
Keywords: electroencephalography, feature extraction, epilepsy, prediction algorithms, biological neural networks, brain modeling, deep learning, intracranial electroencephalogram (iEEG), seizure prediction, convolution neural networks
Journal or Publication Title: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 1558-0210
DOI / ID Number: 10.1109/TNSRE.2020.3035836
Copyright Information:

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Related URLs:
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page
TOP