IEIE Transactions on Smart Processing and Computing

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Time-Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Birth

  • Ryu, Jiwoo (Department of Computer Engineering, Kwangwoon University) ;
  • Park, Cheolsoo (Department of Computer Engineering, Kwangwoon University)
  • Received : 2014.11.20
  • Accepted : 2015.02.12
  • Published : 2015.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, a novel method for the classification of term and preterm birth is proposed based on time-frequency analysis of electrohysterogram (EHG) using multivariate empirical mode decomposition (MEMD). EHG is a promising study for preterm birth prediction, because it is low-cost and accurate compared to other preterm birth prediction methods, such as tocodynamometry (TOCO). Previous studies on preterm birth prediction applied prefilterings based on Fourier analysis of an EHG, followed by feature extraction and classification, even though Fourier analysis is suboptimal to biomedical signals, such as EHG, because of its nonlinearity and nonstationarity. Therefore, the proposed method applies prefiltering based on MEMD instead of Fourier-based prefilters before extracting the sample entropy feature and classifying the term and preterm birth groups. For the evaluation, the Physionet term-preterm EHG database was used where the proposed method and Fourier prefiltering-based method were adopted for comparative study. The result showed that the area under curve (AUC) of the receiver operating characteristic (ROC) was increased by 0.0351 when MEMD was used instead of the Fourier-based prefilter.

Keywords

References

  1. World Health Organization, Born Too Soon: The Global action Report on Preterm birth 2012, 1-111
  2. Robert E. Garfield, Miha Lucovnik, Ruben J. Kuon, "Diagnosis and Effective Management of Preterm Labor," MGM J. Med. Sci., vol. 2014, no. 1, pp. 22-37 https://doi.org/10.5005/jp-journals-10036-1005
  3. Miha Luconvik, Z. Novak-Antolic and Robert E. Garfield, "Use of Non-invasive Uterine Electromyography in the Diagnosis of Preterm Labour," Facts, Views & Vis. in Obgyn., vol. 2012, no. 4
  4. Cheolsoo Park, David Looney, Naveed ur Rehman, Alireza Ahrabian, and Danilo P. Mandic, "Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition," IEEE Trans. on Neural Systems and Rehab. Eng., vol. 2013, no. 21
  5. Robert E. Garfield, William L. Maner, Lyn B. MacKay, Dietmar Schlembach, and George R. Saade, "Comparing uterine electromyography activity of antepartum patients versus term labor patients," American Journal of Obstetrics and Gynecology, vol. 2005, no. 193, pp. 23-9 https://doi.org/10.1016/j.ajog.2005.01.050
  6. Shao Q. Shi, William L. Maner, Lynette B. Mackay and Robert E. Garfield, "Identification of term and pre-term labor in rats using artificial neural networks on uterine emg signals," American Journal of Obstetrics and Gynecology, vol. 2008, no. 198, pp. 235.e1-234.e4 https://doi.org/10.1016/j.ajog.2007.08.039
  7. J. G. Planes, J. P. Morucci, H. Grandjean, and R. Favretto, "External recording and processing of fast electrical activity of the uterus in human parturition," Med. Biol. Eng. Comput., vol. 1984, no. 22, pp. 585-591 https://doi.org/10.1007/BF02443874
  8. Robert E. Garfield, William L. Maner, Lyn B. MacKay, Dietmar Schlembach, and George R. Saade, "Comparing uterine electromyography activity of antepartum patients versus term labor patients," American Journal of Obstetrics and Gynecology, vol. 2005, no. 193, pp. 23-9 https://doi.org/10.1016/j.ajog.2005.01.050
  9. Mohamad O. Diab, Catherine Marque, and Mohamad A. Khalil, "Classification of uterine EMG signals: comparison between AR model and statistical classification method," Int. Jounal of Comput. Cog., vol. 2007, no. 5, pp. 8-14
  10. Bassam Moslem, Mohamad Diab, Mohamad Khalil, and Catherine Marque, "Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals," EURASIP Journal on Advances in Signal Processing, vol. 2012, no. 167
  11. S. Mohammad-Sina Baghamoradi, Mohsen Naji Aryadoost H., "Evaluation of Cepstral Analysis of EHG Signals to Prediction of Preterm Labor," 18th Iranian Conf. on BioMed. Eng., vol. 2011, pp. 81-83
  12. SooYoung Sim, HoSuk Ryu, HanByul Kim, JungMin Han, and KwangSuk Park, "Evaluation of Electrohysterogram Feature Extraction to Classify the Preterm and Term Delivery Groups," The 15th Int. Conf. on Biomed. Eng., vol. 2013, pp. 675-678
  13. Paul Fergus, Paulin Cheung, Abir Hussain, Dhiya Al-Jumeily, Chelsea Dobbins, and Shamaila Iram, "Prediction of Preterm Deliveries form EHG Signals Using Machine Learning", vol. 2013, no. 8
  14. Saffa M, Naeem, Ahmed F. Seddik and Mohamed A. Eldosoky, "New Technique based on uterine electromyography nonlinearity for preterm delivery detection," Journal of Eng. And Tech. Research, vol. 2014, no. 6
  15. G. Fele-Zorz, G. Kavsek, Z. Novak-Antolic, 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," Med. Biol. Eng. Comput., vol. 2008, no. 46, pp. 911-922 10 https://doi.org/10.1007/s11517-008-0350-y
  16. Ivan Verdenik, Marjan Pajntar, Brane Leskosek, "Uterine electrical activity as predictor of preterm birth in women with preterm contractions," European Jounal of Obsterics Gynecology and Reproductive Biology, vol. 2001, no. 95
  17. Catalin Buhimschi, B. Boyle, and Robert E. Garfield, "Electrical Activity of the Human Uterus During Pregnancy as Recorded from the Abdominal Surface," Obstertrics and Gynecology, vol. 1997, no. 90, pp. 102-111 https://doi.org/10.1016/S0029-7844(97)83837-9
  18. Branimir Leskosek and Marjan Pajntar, "Electromyography of the pregnant uterus in humans and sheep," 2nd European Med. Biol. Eng. Conf., vol. 2002, no. 3, pp. 544-545
  19. Mohamad O. Diab, Amira El-Merhie, Nour El-Halabi, Layal Khoder, "Classification of uterine EMG signals using supervised classification method," J. Biol. Sci. and Eng., vol. 2010, no. 3, pp. 837-842 https://doi.org/10.4236/jbise.2010.39113
  20. Wiliam L. Maner and Robert E. Garfield, "Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data," Ann. Biomed. Eng., vol. 2007, no. 35, pp. 365-473
  21. Miha Lucovnik, William L. Manver, Linda R. Chambliss, Richard Blumrick, James Balducci, Ziva Novak-Antolic, Robert E. Garfield, "Noninvasive Uterine Electromyography For Prediction of Preterm Delivery," American Journal of Obstetrics and Gynecology, vol. 2011, no. 204, pp. 228.e1-228.10 https://doi.org/10.1016/j.ajog.2010.09.024
  22. Dima Alamedine, Mohamad Khalil, Catherine Marque, "Parameters Extraction and Monitoring in Uterine EMG Signals. Detection of Preterm Deliveries," IRBM, vol. 2013, no. 34, pp. 322-325 https://doi.org/10.1016/j.irbm.2013.08.003
  23. Bassam Moslem, Mohamad Khalil, Catherine Marque, Mohamad Diab, "Energy Distribution Analysis of Uterine Electromyography Signals," Journal of Medical and Biological Engineering, vol. 2010, no. 30, pp. 361-366 https://doi.org/10.5405/jmbe.768
  24. Sondhiya Arora and Girisha Garg, "A Novel Scheme to Classify EHG Signal for Term and Pre-Term Pregnancy," Analysis International Journal of Computer Applications, vol. 2012, no. 51, pp. 37-41

Cited by

  1. Multivariate Time–Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Labor vol.14, pp.2, 2019, https://doi.org/10.1007/s42835-019-00118-9