ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Comparison of wavelet-based decomposition and empirical mode decomposition of electrohysterogram signals for preterm birth classification

  • Received : 2021.06.26
  • Accepted : 2021.12.01
  • Published : 2022.10.10
Download PDF
⟨ Previous Next ⟩

Abstract

Signal decomposition is a computational technique that dissects a signal into its constituent components, providing supplementary information. In this study, the capability of two common signal decomposition techniques, including wavelet-based and empirical mode decomposition, on preterm birth classification was investigated. Ten time-domain features were extracted from the constituent components of electrohysterogram (EHG) signals, including EHG subbands and EHG intrinsic mode functions, and employed for preterm birth classification. Preterm birth classification and anticipation are crucial tasks that can help reduce preterm birth complications. The computational results show that the preterm birth classification obtained using wavelet-based decomposition is superior. This, therefore, implies that EHG subbands decomposed through wavelet-based decomposition provide more applicable information for preterm birth classification. Furthermore, an accuracy of 0.9776 and a specificity of 0.9978, the best performance on preterm birth classification among state-of-the-art signal processing techniques, were obtained using the time-domain features of EHG subbands.

Keywords

Acknowledgement

This work is supported by a TRF Research Career Development Grant, jointly funded by the Thailand Research Fund (TRF) and the Ubon Ratchathani University, under the Contract No. RSA6180041.

References

  1. A. Grossman and J. Morlet, Decomposition of hardy functions into square integrable wavelets of constant shape, SIAM J. Math. Anal. 15 (1984), 723-736. https://doi.org/10.1137/0515056
  2. I. Daubechies, Orthonormal bases of compactly supported wavelets, Commun. Pure Appl. Math. 41 (1988), 909-996. https://doi.org/10.1002/cpa.3160410705
  3. S. G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell. 11 (1989), 674-693. https://doi.org/10.1109/34.192463
  4. I. Daubechies, Ten lectures on wavelets, Society for industrial and applied mathematics, PA, USA, 1992. https://doi.org/10.1137/1.9781611970104
  5. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. Royal Soc. A 454 (1998), 903-995. https://doi.org/10.1098/rspa.1998.0193
  6. WHO, WHO: recommended definitions, terminology and format for statistical tables related to the perinatal period and use of a new certificate for cause of perinatal deaths, Acta Obstetricia et Gynecologica Scandinavica 56 (1976), 247-253.
  7. H. Blencowe, S. Cousens, D. Chou, M. Oestergaard, L. Say, A. -B. Moller, M. Kinney, and J. Lawn, Born too soon: the global epidemiology of 15 million preterm births, Reproductive Health 10 (2013), S2. https://doi.org/10.1186/1742-4755-10-S1-S2
  8. Mayo Clinic, Premature birth, 2021. https://www.mayoclinic.org/diseases-conditions/premature-birth/symptoms-causes/syc-20376730
  9. World Health Organization, Preterm birth, 2018. https://www.who.int/news-room/fact-sheets/detail/preterm-birth
  10. Centers for Disease Control and Prevention, Preterm birth, 2020. https://www.cdc.gov/reproductivehealth/maternalinfanthealth/pretermbirth.htm
  11. T. Y. Euliano, M. T. Nguyen, S. Darmanjian, S. P. McGorray, N. Euliano, A. Onkala, and A. R. Gregg, Monitoring uterine activity during labor: a comparison of 3 methods, Am. J. Obstetrics Gynecology 208 (2013), 66.e1-e6.
  12. K. Thijssen, M. Vlemminx, M. Westerhuis, J. P. Dieleman, M. B. Van der Hout-Van der Jagt, and S. G. Oei, Uterine monitoring techniques from patients' and users' perspectives, AJP Reports 8 (2018), e184-e191. https://doi.org/10.1055/s-0038-1669409
  13. F. Jager, S. Libensek, and K. Gersak, Characterization and automatic classification of preterm and term uterine records, PLoS ONE 13 (2018), e0202125. https://doi.org/10.1371/journal.pone.0202125
  14. C. Marque, J. M. Duchene, S. Leclercq, G. S. Panczer, and J. Chaumont, Uterine EHG processing for obstetrical monitoring, IEEE Trans. Biomed. Eng. 333 (1986), 1182-1187.
  15. C. Buhimschi, M. B. Boyle, and R. E. Garfield, Electrical activity of human uterus during pregnancy as recorded from the abdominal surface, Obstet. Gynecol. 90 (1997), 102-111. https://doi.org/10.1016/S0029-7844(97)83837-9
  16. I. Verdenik, M. Pajntar, and B. Leskosek, Uterine electrical activity as predictor of preterm birth in women with preterm contractions, Eur. J. Obstet. Gynecol. 95 (2001), 149-153. https://doi.org/10.1016/S0301-2115(00)00418-8
  17. W. L. Maner, R. E. Garfield, H. Maul, G. Olson, and G. Saade, Predicting term and preterm delivery with transabdominal uterine electromyography, Obstetrics Gynecology 101 (2003), 1254-1260.
  18. H. de Lau, C. Rabotti, H. P. Oosterbaan, M. Mischi, and G. S. Oei, Study protocol: PoPE-prediction of preterm delivery by electrohysterography, BMC Pregnancy Childbirth 14 (2014), 192. https://doi.org/10.1186/1471-2393-14-192
  19. C. Rabotti and M. Mischi, Propagation of electrical activity in uterine muscle during pregnancy: a review, Acta Physiologica 213 (2015), 406-416. https://doi.org/10.1111/apha.12424
  20. H. Leman, C. Marque, and J. Gondry, Use of the electrohysterogram signal for characterization of contractions during pregnancy, IEEE Trans. Biomed. Eng. 46 (1999), 1222-1229. https://doi.org/10.1109/10.790499
  21. W. L. Maner and R. E. Garfield, Identification of human term and preterm labor using artificial neural networks on uterine electromyography data, Ann. Biomed. Eng. 35 (2007), 465-473. https://doi.org/10.1007/s10439-006-9248-8
  22. M. Lucovnik, W. L. Maner, L. R. Chambliss, R. Blumrick, J. Balducci, Z. Novak-Antolic, and R. E. Garfield, Noninvasive uterine electromyography for prediction of preterm delivery, Am. J. Obstetrics Gynecology 204 (2011), 228.e1-10.
  23. C. K. Marque, J. Terrien, S. Rihana, and G. Germain, Preterm labour detection by use of a biophysical marker: the uterine electrical activity, BME Pregnancy Childbirth 7 (2007), S5. https://doi.org/10.1186/1471-2393-7-S1-S5
  24. M. W. C. Vlemminx, K. M. J. Thijssen, G. I. Bajlekov, J. P. Dieleman, M. B. Van der Hout-Van der Jagt, and S. G. Oei, Electrohysterography for uterine monitoring during term labour compared to external tocodynamometry and intra-uterine pressure catheter, Eur. J. Obstetrics Gynecol. Reprod. Biol. 215 (2017), 197-205. https://doi.org/10.1016/j.ejogrb.2017.05.027
  25. C. Hemthanon, and S. Janjarasjitt, Correlation between time-domain features of electrohysterogram data of pregnant women and gestational age. In: K. P. Lin, R. Magjarevic, and P. de Carvalho (eds.), Future trends in biomedical and health informatics and cybersecurity in medical devices. vol. 74, Springer, Cham, 2020.
  26. J. Peng, D. Hao, L. Yang, M. Du, X. Song, H. Jiang, Y. Zhang, and D. Zheng, Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random forest, Biocybern. Biomed. Eng. 40 (2020), 352-362. https://doi.org/10.1016/j.bbe.2019.12.003
  27. U. R. Acharya, V. K. Sudarshan, S. Q. Rong, Z. Tan, C. M. Lim, J. E. Koh, S. Nayak, and S. Bhandary, Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals, Comput. Bio. Med. 85 (2017), 33-42. https://doi.org/10.1016/j.compbiomed.2017.04.013
  28. S. Janjarasjitt, Preterm-term birth classification using EMD-based time-domain features of single-channel electrohysterogram data, Phys. Eng. Sci. Med. 44 (2021), 1151-1159. https://doi.org/10.1007/s13246-021-01051-w
  29. J. Mas-Cabo, Y. Ye-Lin, J. Garcia-Casado, A. Diaz-Martinez, A. Perales-Marin, R. Monfort-Ortiz, A. Roca-Prats, A. Lopez- Corral, and G. Prats-Boluda, Robust characterization of the uterine myoelectrical activity in different obstetric scenarios, Entropy 22 (2020), 743. https://doi.org/10.3390/e22070743
  30. G. FeleZorz, 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 preterm delivery groups, Med. Biol. Eng. Comput. 46 (2008), 911-922. https://doi.org/10.1007/s11517-008-0350-y
  31. P. Fergus, P. Cheung, A. Hussain, D. Al-Jumeily, C. Dobbins, and S. Iram, Prediction of preterm deliveries from EHG signals using machine learning, PLoS ONE 8 (2013), e77154. https://doi.org/10.1371/journal.pone.0077154
  32. P. Fergus, I. Idowu, A. Hussain, and C. Dobbins, Advanced artificial neural network classification for detecting preterm births using EHG records, Neurocomput. 188 (2016), 42-49. https://doi.org/10.1016/j.neucom.2015.01.107
  33. C. Hemthanon and S. Janjarasjitt, Examination of time-domain features of EHG data for preterm-term birth classification, J. Comput. 30 (2019), 41-54.
  34. D. Alamedine, M. Khalil, and C. Marque, Comparison of different EHG feature selection methods for the detection of preterm labor, Computat. Math. Methods Med. 2013 (2013). https://doi.org/10.1155/2013/485684
  35. A. Diab, M. Hassan, C. Marque, and B. Karlsson, Performance analysis of four nonlinearity analysis methods using a model with variable complexity and application to uterine EMG signals, Med. Eng. Phys. 36 (2014), 761-767. https://doi.org/10.1016/j.medengphy.2014.01.009
  36. M. Hassan, J. Terrien, C. Marque, and B. Karlsson, Comparison between approximate entropy, correntropy and time reversibility: application to uterine electromyogram signals, Med. Eng. Phys. 33 (2011), 980-986. https://doi.org/10.1016/j.medengphy.2011.03.010
  37. A. Smrdel and F. Jager, Separating sets of term and pre-term uterine EMG records, Physiol. Meas. 36 (2015), 341-355. https://doi.org/10.1088/0967-3334/36/2/341
  38. S. Janjarasjitt, Examination of single wavelet-based features of EHG signals for preterm birth classification, IAENG Int. J. Comput. Sci. 44 (2017), 212-218.
  39. S. Janjarasjitt, Evaluation of performance on preterm birth classification using single wavelet-based features of EHG signals, (Biomedical Engineering International Conference, Hokkaido, Japan), 2017, pp. 1-4. https://doi.org/10.1109/BMEiCON.2017.8229118
  40. B. Moslem, M. Diab, M. Khalil, and C. Marque, Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals, EURASIP J. Adv. Signal Process. 2012 (2012), 167. https://doi.org/10.1186/1687-6180-2012-167
  41. P. Ren, S. Yao, J. Li, P. A. Valdes-Sosa, and K. M. Kendrick, Improved prediction of preterm delivery using empirical mode decomposition analysis of uterine electromyography signals, PLoS ONE 10 (2015), e0132116. https://doi.org/10.1371/journal.pone.0132116
  42. L. Chen and Y. Hao, Feature extraction and classification of EHG between pregnancy and labour group using Hilbert-Huang transform and extreme learning machine, Computat. Math. Methods Med. 2017 (2017), https://doi.org/10.1155/2017/7949507
  43. F. J. Ferri, P. Pudil, M. Hatef, and J. Kittler, Comparative study of techniques for large-scale feature selection, Mach. Intell. Pattern Recogn. 16 (1994), 403-413.
  44. P. Pudil, J. Novovicova, and J. Kittler, Floating search methods in feature selection, Pattern Recogn. Lett. 15 (1994), 1119-1125. https://doi.org/10.1016/0167-8655(94)90127-9
  45. S. Mallat, A wavelet tour of signal processing, 1st ed., Academic Press, San Diego, 1998.
  46. M. Vetterli and C. Herley, Wavelets and filter banks: theory and design, IEEE Trans. Signal Process. 40 (1992), 2207-2232. https://doi.org/10.1109/78.157221
  47. R. Fontugne, P. Borgnat, and P. Flandrin, Online empirical mode decomposition, (IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, LA, USA), Mar. 2017, pp. 4306-4310.
  48. N. E. Huang and S. S. Shen, Hilbert-Huang transform and its applications, 2nd ed., World Scientific, New Jersey, 2014.
  49. N. E. Huang, M.-L. C. Wu, S. R. Long, S. S. P. Shen, W. Qu, P. Gloersen, and K. L. Fan, A confidence limit for the empirical mode decomposition and Hilbert spectral analysis, Proc. Royal Soc. A 459 (2003), 2317-2345. https://doi.org/10.1098/rspa.2003.1123
  50. F. Jager, Term-Preterm EHG Database, 2012. https://www.physionet.org/content/tpehgdb/1.0.1/
  51. A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals, Circulation 101 (2000), no. 23, e215-e220.
  52. K. S. Kim, H. H. Choi, C. S. Moon, and C. W. Mun, Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions, Current Appl. Phys. 11 (2011), 740-745. https://doi.org/10.1016/j.cap.2010.11.051
  53. M. A. Oskoei and H. Hu, Support vector machine-based classification scheme for myoelectric control applied to upper limb, IEEE Trans. Biomed. Eng. 55 (2008), 1956-1965. https://doi.org/10.1109/TBME.2008.919734
  54. D. Tkach, H. Huang, and T. A. Kuiken, Study of stability of time-domain features for electromyographic pattern recognition, J. NeuroEng. Rehabilitation 7 (2010), 21. https://doi.org/10.1186/1743-0003-7-21
  55. M. Zardoshti-Kermani, B. C. Wheeler, K. Badie, and R. M. Hashemi, EMG feature evaluation for movement control of upper extremity prostheses, IEEE Trans. Rehabilitation Eng. 3 (1995), 324-333. https://doi.org/10.1109/86.481972
  56. I. Guyon and A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res. 3 (2003), 1157-1182.
  57. T. Fawcett, An introduction to ROC analysis, Pattern Recogn. Lett. 27 (2006), 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
  58. A. M. Kaleem and R. D. Kokate, Prediction of pre-term groups from EHG signals using optimal multi-kernel SVM, J. Ambient Intell. Humanized Comput. 12 (2021), 3689-3703. https://doi.org/10.1007/s12652-019-01648-w
  59. 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, Neurocomput. 151 (2015), 963-974. https://doi.org/10.1016/j.neucom.2014.03.087
  60. H. He, Y. Bai, E. A. Garcia, and S. Li, ADASYN: adaptive synthetic sampling approach for imbalanced learning, (IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong), 2008, pp. 1322-1328.