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Scientific Programming
Volume 2016, Article ID 5642856, 9 pages
Research Article

Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals

1School of Automation, Chongqing University, Chongqing, China
2Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing University, Ministry of Education, Chongqing, China
3School of Software Engineering, Chongqing University, Chongqing, China

Received 16 June 2016; Accepted 17 August 2016

Academic Editor: X. Wang

Copyright © 2016 Kai Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [5 citations]

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  • Yi Chai, Qiu Tang, Hao Ren, Jian-Feng Qu, and Xin Ye, “Deep learning for fault diagnosis: The state of the art and challenge,” Kongzhi yu Juece/Control and Decision, vol. 32, no. 8, pp. 1345–1358, 2017. View at Publisher · View at Google Scholar
  • Wendong Zhang, and Chenyang Xue, “Research on Working State Monitoring System of Coal Mine Machinery,” Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis, vol. 38, no. 5, pp. 883–889, 2018. View at Publisher · View at Google Scholar
  • Alex Zhavoronkov, Konstantin Romantsov, Alex Ostrovski, Polina Mamoshina, Yury Yanovich, Alex Botezatu, Pavel Prikhodko, Eugene Izumchenko, Alexander Zhebrak, Iraneus Obioma Ogu, Lucy Ojomoko, and Alexander Aliper, “Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare,” Oncotarget, vol. 9, no. 5, pp. 5665–5690, 2018. View at Publisher · View at Google Scholar
  • Guanjie Huang, Chao-Hsien Chu, and Xiaodan Wu, “A deep learning-based method for sleep stage classification using physiological signal,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10983, pp. 249–260, 2018. View at Publisher · View at Google Scholar
  • Ravindra Kumar, Megha Kumar, and Upasna Joshi, “Data Mining-Based Student’s Performance Evaluator,” Intelligent Communication, Control and Devices, vol. 989, pp. 719–726, 2019. View at Publisher · View at Google Scholar