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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 7949507, 9 pages
https://doi.org/10.1155/2017/7949507
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
  • Nafissa Sadi-Ahmed, Baya Kacha, Hamza Taleb, and Malika Kedir-Talha, “Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records,” Journal of Medical Systems, vol. 41, no. 12, 2017. View at Publisher · View at Google Scholar
  • Shui-Hua Wang, Khan Muhammad, Preetha Phillips, Zhengchao Dong, and Yu-Dong Zhang, “Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension,” Journal of Ambient Intelligence and Humanized Computing, 2017. View at Publisher · View at Google Scholar
  • Franc Jager, Sonja Libenšek, and Ksenija Geršak, “Characterization and automatic classification of preterm and term uterine records,” Plos One, vol. 13, no. 8, pp. e0202125, 2018. View at Publisher · View at Google Scholar
  • Aleksandra Zec, Katarina Mladenovic, Nevena Radin, Danica Despotovic, and Tatjana Loncar Turukalo, “A Machine Learning Approach for an Early Prediction of Preterm Delivery,” SISY 2018 - IEEE 16th International Symposium on Intelligent Systems and Informatics, Proceedings, pp. 265–270, 2018. View at Publisher · View at Google Scholar
  • Gustavo Pacheco-López, Lenin Pavón, Rodrigo Ayala-Yáñez, José Javier Reyes-Lagos, Juan Carlos Echeverría, María Teresa García-González, Claudia Ivette Ledesma-Ramírez, Jorge Escalante-Gaytán, Ramón González-Camarena, Miguel Ángel Peña-Castillo, and Enrique Becerril-Villanueva, “Associations of Immunological Markers and Anthropometric Measures with Linear and Nonlinear Electrohysterographic Parameters at Term Active Labor,” Advances in Neuroimmune Biology, vol. 7, no. 1, pp. 27–36, 2018. View at Publisher · View at Google Scholar
  • 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