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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 7949507, 9 pages
Research Article

Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

Lili Chen1,2 and Yaru Hao1,2

1School of Mechatronics & Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China
2School of Chongqing Key Laboratory of Urban Rail Transit Vehicle System Integration and Control, Chongqing Jiaotong University, Chongqing 400074, China

Correspondence should be addressed to Lili Chen; nc.ude.utjqc@225ililc

Received 4 August 2016; Revised 7 November 2016; Accepted 26 January 2017; Published 19 February 2017

Academic Editor: Hiro Yoshida

Copyright © 2017 Lili Chen and Yaru Hao. 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 [9 citations]

The following is the list of published articles that have cited the current article.

  • Sandra Marquez-Figueroa, Yuriy S. Shmaliy, Oscar Ibarra-Manzano, Carlos Lastre-Dominguez, and Miguel Vazquez-Olguin, “Procedure for Removing Artifacts from EMG Signals Envelope Assuming CMN,” 2019 16th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1–6, . View at Publisher · View at Google Scholar
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  • Punitha Namadurai, Vardhini Padmanabhan, and Ramakrishnan Swaminathan, “Multifractal Analysis of Uterine Electromyography Signals for the Assessment of Progression of Pregnancy in Term Conditions,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 5, pp. 1972–1979, 2019. View at Publisher · View at Google Scholar
  • T. Athira, and P. Shaniba Asmi, “Analysis of Unipolar and Bipolar 4x4 EHG Signal for Classifying Uterine Contraction,” Biomedical and Pharmacology Journal, vol. 12, no. 2, pp. 1009–1014, 2019. View at Publisher · View at Google Scholar
  • Hu, and Zebo Yu, “Diagnosis of mesothelioma with deep learning,” Oncology Letters, vol. 17, no. 2, pp. 1483–1490, 2019. View at Publisher · View at Google Scholar