Le, Thanh Xuyen and Le, Trung Thanh and Dinh, Van Viet and Tran, Quoc Long and Nguyen, Linh Trung and Nguyen, Duc Thuan (2018) Deep Learning for Epileptic Spike Detection. VNU Journal of Science: Computer Science and Communication Engineering, 33 (2). pp. 1-13. ISSN 2588-1086
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Abstract
In the clinical diagnosis of epilepsy using electroencephalogram (EEG) data, an accurate automatic epileptic spikes detection system is highly useful and meaningful in that the conventional manual process is not only very tedious and time-consuming, but also subjective since it depends on the knowledge and experience of the doctors. In this paper, motivated by significant advantages and lots of achieved successes of deep learning in data mining, we apply Deep Belief Network (DBN), which is one of the breakthrough models laid the foundation for deep learning, to detect epileptic spikes in EEG data. It is really useful in practice because the promising quality evaluation of the spike detection system is higher than $90$\%. In particular, to construct accurate detection model for non-spikes and spikes, a new set of detailed features of epileptic spikes is proposed. These features were then fed to the DBN which is modified from a generative model into a discriminative model to aim at classification accuracy. The experiment results indicate that it is possible to use deep learning models for epileptic spike detection with very high performance in item of sensitivity, selectivity, specificity and accuracy 92.82%, 97.83% , 96.41%, and 96.87%, respectively.
| Item Type: | Article |
|---|---|
| Subjects: | Electronics and Communications Information Technology (IT) |
| Divisions: | Advanced Insitute of Engineering and Technology (AVITECH) Faculty of Electronics and Telecommunications (FET) Faculty of Information Technology (FIT) |
| Depositing User: | Long Trần Quốc |
| Date Deposited: | 30 Nov 2020 04:44 |
| Last Modified: | 30 Nov 2020 04:44 |
| URI: | http://eprints.uet.vnu.edu.vn/eprints/id/eprint/4085 |
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