eprintid: 4085 rev_number: 10 eprint_status: archive userid: 335 dir: disk0/00/00/40/85 datestamp: 2020-11-30 04:44:13 lastmod: 2020-11-30 04:44:13 status_changed: 2020-11-30 04:44:13 type: article succeeds: 2978 metadata_visibility: show creators_name: Le, Thanh Xuyen creators_name: Le, Trung Thanh creators_name: Dinh, Van Viet creators_name: Tran, Quoc Long creators_name: Nguyen, Linh Trung creators_name: Nguyen, Duc Thuan creators_id: xuyen.lethanh@hust.edu.vn creators_id: letrungthanhtbt@gmail.com creators_id: dinhvanviet2608@gmail.com creators_id: tqlong@vnu.edu.vn creators_id: linhtrung@vnu.edu.vn creators_id: thuan.nguyenduc@hust.edu.vn title: Deep Learning for Epileptic Spike Detection ispublished: pub subjects: ECE subjects: IT divisions: avitech divisions: fac_fet divisions: fac_fit 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. date: 2018-03-03 date_type: published publisher: VNU official_url: http://www.jcsce.vnu.edu.vn/index.php/jcsce/article/view/156 id_number: https://doi.org/10.25073/2588-1086/vnucsce.156 full_text_status: public publication: VNU Journal of Science: Computer Science and Communication Engineering volume: 33 number: 2 pagerange: 1-13 refereed: TRUE issn: 2588-1086 citation: 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 document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4085/1/156-1-728-2-10-20201117.pdf