Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2017 (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

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.

Linked References

  1. S. A. Hamilton and C. Mullan, “Management of preterm labour,” Obstetrics, Gynaecology and Reproductive Medicine, vol. 26, no. 1, pp. 12–19, 2016. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. H. Neggers, “The relationship between preterm birth and underweight in Asian women,” Reproductive Toxicology, vol. 56, pp. 170–174, 2015. View at Publisher · View at Google Scholar · View at Scopus
  3. World Health Organization, Born Too Soon: The Global Action Report on Preterm Birth, WHO, 2012.
  4. A. S. Butler and R. E. Behrman, Preterm Birth:: Causes, Consequences, and Prevention, National Academies Press, Washington, DC, USA, 2007. View at Publisher · View at Google Scholar
  5. A. J. Hussain, P. Fergus, H. Al-Askar, D. Al-Jumeily, and F. Jager, “Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women,” Neurocomputing, vol. 151, no. 3, pp. 963–974, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. D. Alamedine, A. Diab, C. Muszynski, B. Karlsson, M. Khalil, and C. Marque, “Selection algorithm for parameters to characterize uterine EHG signals for the detection of preterm labor,” Signal, Image and Video Processing, vol. 8, no. 6, pp. 1169–1178, 2014. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Sotiriadis, S. Papatheodorou, A. Kavvadias, and G. Makrydimas, “Transvaginal cervical length measurement for prediction of preterm birth in women with threatened preterm labor: a meta-analysis,” Ultrasound in Obstetrics and Gynecology, vol. 35, no. 1, pp. 54–64, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. I. Hudić, B. Stray-Pedersen, J. Szekeres-Bartho et al., “Maternal serum progesterone-induced blocking factor (PIBF) in the prediction of preterm birth,” Journal of Reproductive Immunology, vol. 109, pp. 36–40, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. D. S. Abbott, S. K. Radford, P. T. Seed, R. M. Tribe, and A. H. Shennan, “Evaluation of a quantitative fetal fibronectin test for spontaneous preterm birth in symptomatic women,” American Journal of Obstetrics and Gynecology, vol. 208, no. 2, pp. 122.e1–122.e6, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. F. Riboni, A. Vitulo, M. Dell'Avanzo, M. Plebani, G. Battagliarin, and D. Paternoster, “Biochemical markers predicting pre-term delivery in symptomatic patients: phosphorylated insulin-like growth factor binding protein-1 and fetal fibronectin,” Archives of Gynecology and Obstetrics, vol. 284, no. 6, pp. 1325–1329, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. L. A. Bolt, M. Chandiramani, A. De Greeff, P. T. Seed, J. Kurtzman, and A. H. Shennan, “The value of combined cervical length measurement and fetal fibronectin testing to predict spontaneous preterm birth in asymptomatic high-risk women,” Journal of Maternal-Fetal and Neonatal Medicine, vol. 24, no. 7, pp. 928–932, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. C. Marque, J. M. G. Duchene, S. Leclercq, G. S. Panczer, and J. Chaumont, “Uterine EHG processing for obstetrical monitoring,” IEEE Transactions on Biomedical Engineering, vol. 33, no. 12, pp. 1182–1187, 1986. View at Publisher · View at Google Scholar · View at Scopus
  13. R. E. Garfield, “Method and apparatus for the recording and analysis of uterine electrical activity from the abdominal surface,” 1996.
  14. W. L. Maner, R. E. Garfield, H. Maul, G. Olson, and G. Saade, “Predicting term and preterm delivery with transabdominal uterine electromyography,” Obstetrics and Gynecology, vol. 101, no. 6, pp. 1254–1260, 2003. View at Publisher · View at Google Scholar · View at Scopus
  15. B. Moslem, M. Khalil, C. Marque, M. O. Diab, and M. Hassan, “Monitoring the progress of pregnancy and detecting labor using uterine electromyography,” in Proceedings of the International Symposium on Bioelectronics and Bioinformatics, Melbourne, Australia, December 2009.
  16. C. Marque and J. Duchene, “Human abdominal EHG processing for uterine contraction monitoring,” Biotechnology, vol. 11, pp. 187–226, 1989. View at Google Scholar · View at Scopus
  17. M. Khalil and J. Duchêne, “Uterine EMG analysis: a dynamic approach for change detection and classification,” IEEE Transactions on Biomedical Engineering, vol. 47, no. 6, pp. 748–756, 2000. View at Publisher · View at Google Scholar · View at Scopus
  18. B. Moslem, M. Khalil, C. Marque, and M. O. Diab, “Complexity analysis of the uterine electromyography,” in Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '10), pp. 2802–2805, IEEE, Buenos Aires, Argentina, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Diab, M. Hassan, C. Marque, and B. Karlsson, “Quantitative performance analysis of four methods of evaluating signal nonlinearity: application to uterine EMG signals,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC '12), pp. 1045–1048, San Diego, Calif, USA, August 2012. View at Publisher · View at Google Scholar
  20. P. Fergus, I. Idowu, A. Hussain, and C. Dobbins, “Advanced artificial neural network classification for detecting preterm births using EHG records,” Neurocomputing, vol. 188, pp. 42–49, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. G. Fele-Žorž, G. Kavšek, Ž. Novak-Antolič, and F. Jager, “A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups,” Medical and Biological Engineering and Computing, vol. 46, no. 9, pp. 911–922, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. D. Alamedine, M. Khalil, and C. Marque, “Parameters extraction and monitoring in uterine EMG signals. Detection of preterm deliveries,” IRBM, vol. 34, no. 4-5, pp. 322–325, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. M. O. Diab, C. Marque, and M. A. Khalil, “Classification for uterine EMG Signals: comparison between AR model and statistical classification method,” International Journal of Computational Cognition, vol. 5, no. 1, pp. 8–14, 2007. View at Google Scholar
  24. H. Ocak, “Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy,” Expert Systems with Applications, vol. 36, no. 2, pp. 2027–2036, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. V. Joshi, R. B. Pachori, and A. Vijesh, “Classification of ictal and seizure-free EEG signals using fractional linear prediction,” Biomedical Signal Processing and Control, vol. 9, no. 1, pp. 1–5, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. C. Guerrero-Mosquera, A. M. Trigueros, J. I. Franco, and Á. Navia-Vázquez, “New feature extraction approach for epileptic EEG signal detection using time-frequency distributions,” Medical & Biological Engineering & Computing, vol. 48, no. 4, pp. 321–330, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. R. Schuyler, A. White, K. Staley, and K. J. Cios, “Epileptic seizure detection,” IEEE Engineering in Medicine and Biology Magazine, vol. 26, no. 2, pp. 74–81, 2007. View at Publisher · View at Google Scholar · View at Scopus
  28. K. Fu, J. Qu, Y. Chai, and T. Zou, “Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals,” Biomedical Signal Processing and Control, vol. 18, pp. 179–185, 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. Q. Zhu, Y. Wang, and G. Shen, “Research and comparison of time-frequency techniques for nonstationary signals,” Journal of Computers, vol. 7, no. 4, pp. 954–958, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. K. Fu, J. Qu, Y. Chai, and Y. Dong, “Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM,” Biomedical Signal Processing and Control, vol. 13, no. 1, pp. 15–22, 2014. View at Publisher · View at Google Scholar · View at Scopus
  31. L. Fraiwan, K. Lweesy, N. Khasawneh, H. Wenz, and H. Dickhaus, “Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier,” Computer Methods and Programs in Biomedicine, vol. 108, no. 1, pp. 10–19, 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. K. Kudrynski and P. Strumillo, “Real-time estimation of the spectral parameters of Heart Rate Variability,” Biocybernetics and Biomedical Engineering, vol. 35, no. 4, pp. 304–316, 2015. View at Publisher · View at Google Scholar · View at Scopus
  33. D. Liu, X. Yang, G. Wang et al., “HHT based cardiopulmonary coupling analysis for sleep apnea detection,” Sleep Medicine, vol. 13, no. 5, pp. 503–509, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. S.-Z. Fan, Q. Wei, P.-F. Shi, Y.-J. Chen, Q. Liu, and J.-S. Shieh, “A comparison of patients' heart rate variability and blood flow variability during surgery based on the Hilbert-Huang Transform,” Biomedical Signal Processing and Control, vol. 7, no. 5, pp. 465–473, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. A. Pigorini, A. G. Casali, S. Casarotto et al., “Time-frequency spectral analysis of TMS-evoked EEG oscillations by means of Hilbert-Huang transform,” Journal of Neuroscience Methods, vol. 198, no. 2, pp. 236–245, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. P. Caseiro, R. Fonseca-Pinto, and A. Andrade, “Screening of obstructive sleep apnea using Hilbert-Huang decomposition of oronasal airway pressure recordings,” Medical Engineering & Physics, vol. 32, no. 6, pp. 561–568, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. L. Guo, D. Rivero, J. Dorado, J. R. Rabuñal, and A. Pazos, “Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks,” Journal of Neuroscience Methods, vol. 191, no. 1, pp. 101–109, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. X. Zhang, W. Chen, B. Wang, and X. Chen, “Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization,” Neurocomputing, vol. 167, pp. 260–279, 2015. View at Publisher · View at Google Scholar · View at Scopus
  39. L.-L. Chen, J. Zhang, J.-Z. Zou, C.-J. Zhao, and G.-S. Wang, “A framework on wavelet-based nonlinear features and extreme learning machine for epileptic seizure detection,” Biomedical Signal Processing and Control, vol. 10, no. 1, pp. 1–10, 2014. View at Publisher · View at Google Scholar · View at Scopus
  40. Q. Yuan, W. Zhou, Y. Liu, and J. Wang, “Epileptic seizure detection with linear and nonlinear features,” Epilepsy and Behavior, vol. 24, no. 4, pp. 415–421, 2012. View at Publisher · View at Google Scholar · View at Scopus
  41. Q. Yuan, W. Zhou, S. Li, and D. Cai, “Epileptic EEG classification based on extreme learning machine and nonlinear features,” Epilepsy Research, vol. 96, no. 1-2, pp. 29–38, 2011. View at Publisher · View at Google Scholar · View at Scopus
  42. A. L. Goldberger, L. A. Amaral, L. Glass et al., “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. E215–E220, 2000. View at Publisher · View at Google Scholar · View at Scopus
  43. A. Alexandersson, T. Steingrimsdottir, J. Terrien, C. Marque, and B. Karlsson, “The Icelandic 16-electrode electrohysterogram database,” Scientific Data, vol. 2, Article ID 150017, 2015. View at Publisher · View at Google Scholar
  44. S. Li, W. Zhou, Q. Yuan, S. Geng, and D. Cai, “Feature extraction and recognition of ictal EEG using EMD and SVM,” Computers in Biology and Medicine, vol. 43, no. 7, pp. 807–816, 2013. View at Publisher · View at Google Scholar · View at Scopus
  45. Y. Song and J. Zhang, “Automatic recognition of epileptic EEG patterns via Extreme Learning Machine and multiresolution feature extraction,” Expert Systems with Applications, vol. 40, no. 14, pp. 5477–5489, 2013. View at Publisher · View at Google Scholar · View at Scopus
  46. Y. Song and J. Zhang, “Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine,” Journal of Neuroscience Methods, vol. 257, pp. 45–54, 2016. View at Publisher · View at Google Scholar · View at Scopus
  47. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” in Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 985–990, Budapest, Hungary, July 2004. View at Publisher · View at Google Scholar · View at Scopus
  48. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  49. Y. Song, J. Crowcroft, and J. Zhang, “Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine,” Journal of Neuroscience Methods, vol. 210, no. 2, pp. 132–146, 2012. View at Publisher · View at Google Scholar · View at Scopus
  50. D. Devedeux, C. Marque, S. Mansour, G. Germain, and J. Duchêne, “Uterine electromyography: a critical review,” American Journal of Obstetrics and Gynecology, vol. 169, no. 6, pp. 1636–1653, 1993. View at Publisher · View at Google Scholar · View at Scopus
  51. Y. Kumar, M. L. Dewal, and R. S. Anand, “Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine,” Neurocomputing, vol. 133, pp. 271–279, 2014. View at Publisher · View at Google Scholar · View at Scopus
  52. J.-L. Song, W. Hu, and R. Zhang, “Automated detection of epileptic EEGs using a novel fusion feature and extreme learning machine,” Neurocomputing, vol. 175, pp. 383–391, 2016. View at Publisher · View at Google Scholar · View at Scopus