Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning
Joint Authors
Hu, Dayi
Chen, Sirui
Zhang, Baocan
Xiao, Shixiao
Xiao, Yutian
Wang, Wennan
Chen, Shuaichen
Xu, Gaowei
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-08
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Electroencephalography (EEG) plays an import role in monitoring the brain activities of patients with epilepsy and has been extensively used to diagnose epilepsy.
Clinically reading tens or even hundreds of hours of EEG recordings is very time consuming.
Therefore, automatic detection of seizure is of great importance.
But the huge diversity of EEG signals belonging to different patients makes the task of seizure detection much challenging, for both human experts and automation methods.
We propose three deep transfer convolutional neural networks (CNN) for automatic cross-subject seizure detection, based on VGG16, VGG19, and ResNet50, respectively.
The original dataset is the CHB-MIT scalp EEG dataset.
We use short time Fourier transform to generate time-frequency spectrum images as the input dataset, while positive samples are augmented due to the infrequent nature of seizure.
The model parameters pretrained on ImageNet are transferred to our models.
And the fine-tuned top layers, with an output layer of two neurons for binary classification (seizure or nonseizure), are trained from scratch.
Then, the input dataset are randomly shuffled and divided into three partitions for training, validating, and testing the deep transfer CNNs, respectively.
The average accuracies achieved by the deep transfer CNNs based on VGG16, VGG19, and ResNet50 are 97.75%, 98.26%, and 96.17% correspondingly.
On those results of experiments, our method could prove to be an effective method for cross-subject seizure detection.
American Psychological Association (APA)
Zhang, Baocan& Wang, Wennan& Xiao, Yutian& Xiao, Shixiao& Chen, Shuaichen& Chen, Sirui…[et al.]. 2020. Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1139585
Modern Language Association (MLA)
Zhang, Baocan…[et al.]. Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1139585
American Medical Association (AMA)
Zhang, Baocan& Wang, Wennan& Xiao, Yutian& Xiao, Shixiao& Chen, Shuaichen& Chen, Sirui…[et al.]. Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1139585
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139585