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J-GLOBAL ID：202302264298094721   Reference number：23A0855620

A Deep Learning Approach to Detect Microsleep Using Various Forms of EEG Signal

EEG信号の様々な形態を用いたマイクロスリープを検出するための深層学習アプローチ【JST・京大機械翻訳】

Author (6)： Sangeetha S. K. B. (Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, Tamilnadu, India) ,  Mathivanan Sandeep Kumar (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India) ,  Muthukumaran V. (Department of Mathematics, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur 603203, Tamilnadu, India) ,  Pughazendi N. (Department of Computer Science and Engineering, Panimalar Engineering College, Poonamallee, Chennai 600123, Tamilnadu, India) ,  Jayagopal Prabhu (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India) ,  Uddin Md Salah (Department of Multimedia and Creative Technology, Daffodil International University, Dhaka-1207, Bangladesh)
Material： Mathematical Problems in Engineering (Web)
Volume： 2023  Page： Null  Publication year： 2023 
JST Material Number： U7803A  ISSN： 1024-123X  Document type： Article
Article type： 原著論文  Country of issue： United Kingdom (GBR)  Language： ENGLISH (EN)

Abstract/Point： Electroencephalography (EEG) i...

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Thesaurus term： noise(signal) ,  automation ,  electroencephalogram ,  neural network(biomodel) ,  *sleep ,  power spectrum ,  two dimension ,  logarithm ,  machine learning ,  learning algorithm
Semi thesaurus term： 眠気 ,  計算コスト ,  ハイパーパラメータ ,  *脳波信号 ,  *深層学習 , 【Automatic Indexing@JST】

JST classification (2)： Biometry  (EL03020C) ,  Artificial intelligence  (JE08000Z)

Reference (26)：

• K. R. Parkes, "Age and work environment characteristics in relation to sleep: additive, interactive and curvilinear effects," Applied Ergonomics, vol. 54, pp. 41-50, 2016.
• A. R. Hassan, M. I. H. Bhuiyan, "Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting," Computer Methods and Programs in Biomedicine, vol. 140, pp. 201-210, 2017.
• R. Sharma, R. B. Pachori, A. Upadhyay, "Automatic sleep stages classification based on iterative filtering of electroencephalogram signals," Neural Computing & Applications, vol. 28, no. 10, pp. 2959-2978, 2017.
• Y. Chu, X. Zhao, J. Han, Y. Zhao, J Yao, "SSVEP-based brain-computer interface controlled functional electrical stimulation system for upper extremity rehabilitation," Proceedings of the 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), no. 7, pp. 947-956, Bali, Indonesia, December 2014.
• S. K. B. Sangeetha, R. Dhaya, D. T. Shah, R. Dharanidharan, K. P. S. Reddy, "An empirical analysis of machine learning frameworks for digital pathology in medical science," Journal of Physics: Conference Series, vol. 1767, February 2021.

Terms in the title (4)： EEG信号 ,  形態 ,  スリープ ,  深層学習