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

Citation

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, Seizure prediction using directed transfer function and convolution neural network on intracranial EEG, IEEE Transactions on Neural Systems and Rehabilitation Engineering pp. 1-11. ISSN 1558-0210 (2020) [Refereed Article]


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DOI: doi:10.1109/TNSRE.2020.3035836

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 Details

Item Type:Refereed Article
Keywords:electroencephalography, feature extraction, epilepsy, prediction algorithms, biological neural networks, brain modeling, deep learning, intracranial electroencephalogram (iEEG), seizure prediction, convolution neural networks
Research Division:Information and Computing Sciences
Research Group:Library and information studies
Research Field:Human information behaviour
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:141890
Year Published:2020
Web of Science® Times Cited:14
Deposited By:Information and Communication Technology
Deposited On:2020-12-01
Last Modified:2021-11-23
Downloads:3 View Download Statistics

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