A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups | Medical & Biological Engineering & Computing Skip to main content
Springer Nature Link
Log in

A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Various linear and non-linear signal-processing techniques were applied to three-channel uterine EMG records to separate term and pre-term deliveries. The linear techniques were root mean square value, peak and median frequency of the signal power spectrum and autocorrelation zero crossing; while the selected non-linear techniques were estimation of the maximal Lyapunov exponent, correlation dimension and calculating sample entropy. In total, 300 records were grouped into four groups according to the time of recording (before or after the 26th week of gestation) and according to the total length of gestation (term delivery records—pregnancy duration ≥37 weeks and pre-term delivery records—pregnancy duration <37 weeks). The following preprocessing band-pass Butterworth filters were tested: 0.08–4, 0.3–4, and 0.3–3 Hz. With the 0.3–3 Hz filter, the median frequency indicated a statistical difference between those term and pre-term delivery records recorded before the 26th week (p = 0.03), and between all term and all pre-term delivery records (p = 0.012). With the same filter, the sample entropy indicated statistical differences between those term and pre-term delivery records recorded before the 26th week (p = 0.035), and between all term and all pre-term delivery records (p = 0.011). Both techniques also showed noticeable differences between term delivery records recorded before and after the 26th week (p ≤ 0.001).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Akay M (2001) Nonlinear biomedical signal processing, vol II. Dynamic analysis and modeling. IEEE Inc., New York

  2. Buhimschi C, Boyle MB, Garfield RE (1997) Electrical activity of the human uterus during pregnancy as recorded from the abdominal surface. Obstet Gynecol 90:102–111

    Article  Google Scholar 

  3. Carre P, Leman H, Fernandez C, Marque C (1998) Denoising of the uterine EHG by an undecimated wavelet transform. IEEE Trans Biomed Eng 45(9):1104–1113

    Article  Google Scholar 

  4. Devedeux D, Marque C, Mansour S, Germain G, Duchene J (1993) Uterine electromyography: a critical review. Am J Obstet Gynecol 169(6):1636–1653

    Google Scholar 

  5. Fele-Žorž G (2006) Characterization of emg data. In: Rudel D, Oberžan D (eds) First international workshop on pregnant uterine smooth muscle EMG activity. Institute of biomedical informatics, University of Ljubljana, pp 15–15

  6. Garfield RE, Maner WL, MacKay LB, Schlembach D, Saade GR (2005) Comparing uterine electromyography activity of antepartum patients versus term labor patients. Am J Obstet Gynecol 193(1):23–29

    Article  Google Scholar 

  7. Garfield RE, Maner WL, Maul H, Saade GR (2005a) Use of uterine EMG and cervical LIF in monitoring pregnant patients. Biol J Obstet Gynecol 112(Suppl 1):103–108

    Google Scholar 

  8. Gondry J, Duchgne J, Marque C (1992) First results on uterine EMG monitoring during pregnancy. Int Conf IEEE EMBS 6:2609–2610

    Google Scholar 

  9. Gondry JX, Marque CX, Duchene JX, Cabrol DX (1993) Electrohysterography during pregnancy: preliminary report. Biomed Instrum Technol 27(4):318–324

    Google Scholar 

  10. Iams JD (2003) Prediction and early detection of preterm labor. Am Col Obstet Gynecol 101(2):402–412

    Google Scholar 

  11. Iams JD, Newman RB, Thom EA, Goldenberg RL, Mueller-Heubach E, Moawad A, Sibai BM, Caritis SN, Miodovnik M, Paul RH, Dombrowski MP, Thurnau G, McNellis D (2002) Frequency of uterine contractions and the risk of spontaneous preterm delivery. N Engl J Med 346(4):250–255

    Article  Google Scholar 

  12. Jezewski J, Horoba K, Matonia A, Wrobel J (2005) Quantitative analysis of contraction patterns in electrical activity signal of pregnant uterus as an alternative to mechanical approach. Physiol Meas 26(5):753–767

    Article  Google Scholar 

  13. Kavšek G (2001) Electromiographic activity of the uterus in threatened preterm delivery. Master’s Thesis, University of Ljubljana, Medical faculty, Ljubljana

  14. Lake DE, Richman JS, Griffin MP, Moorman JR (2002) Sample entropy analysis of neonatal heart rate variability. Am J Physiol 283:789–797

    Google Scholar 

  15. Leman H, Marque C, Gondry J (1999) Use of the electrohysterogram signal for characterization of contractions during pregnancy. IEEE Trans Biomed Eng 46(10):1222–1229

    Article  Google Scholar 

  16. Maner WL, Garfield RE (2007) Identification of human term and preterm labor using artificial neural networks on uterine electromyography data. Ann Biomed Eng 35(3):465–473

    Article  Google Scholar 

  17. Maner WL, Garfield RE, Maul H, Olson G, Saade G (2003) Predicting term and preterm delivery with transabdominal uterine electromyography. Obstet Gynecol 101(6):1254–1260

    Article  Google Scholar 

  18. Maner WL, MacKay LB, Saade GR, Garfield RE (2006) Characterization of abdominally acquired uterine electrical signals in humans, using a non-linear analytic method. Med Biol Eng Comput 44(1–2):117–123

    Article  Google Scholar 

  19. Marque C, Duchene J, Leclercq S, Panczer G, Chaumont J (1986) Uterine EHG processing for obstetrical monitoring. IEEE Trans Biomed Eng 33(12):1182–1187

    Article  Google Scholar 

  20. Maul H, Maner WL, Olson G, Saade GR, Garfield RE (2004) Non-invasive transabdominal uterine electromyography correlates with the strength of intrauterine pressure and is predictive of labor and delivery. J Matern Fetal Neonatal Med 15(5):297–301

    Article  Google Scholar 

  21. Meekai ST, Alfirovic Z, Heath VCF, Cicero S, Cacho AM, Williamson PR, Kypros HN (2004) Cervical cerclage for prevention of preterm delivery in women with short cervix: randomised controlled trial. Lancet 363:1849–1853

    Article  Google Scholar 

  22. Nagarajan P, Eswaran H, Wilson J, Murphy P, Lowery C, Preisl H (2003) Analysis of uterine contractions: a dynamical approach. J Matern Fetal Neonatal Med 14:8–20

    Article  Google Scholar 

  23. Rosenstein M, Collins J, De Luca C (1992) A practical method for calculating largest lyapunov exponent from small data sets. Tech rep, Boston University, Neuromuscular research center, Boston

  24. Verdenik I (2002) Multilayer prediction model for preterm delivery. Ph.D. Thesis, University of Ljubljana, Medical faculty, Ljubljana

  25. Verdenik I, Pajntar M, Leskosek B (2001) Uterine electrical activity as predictor of preterm birth in women with preterm contractions. Eur J Obstet Gynecol Reprod Biol 95(2):149–153

    Article  Google Scholar 

Download references

Acknowledgments

This work was financed by the Slovenian Research Agency (ARRS) which provided a grant No. 1000-05-310097 and a research project P3-0124—Metabolic and inborn factors of reproductive health, birth.

Author information

Authors and Affiliations

  1. Faculty of Computer and Information Science, University of Ljubljana, Tržaška 25, 1000, Ljubljana, Slovenia

    G. Fele-Žorž & F. Jager

  2. Department of Obstetrics and Gynecology, Medical Centre Ljubljana, Zaloška 11, 1000, Ljubljana, Slovenia

    G. Kavšek & Ž. Novak-Antolič

Authors
  1. G. Fele-Žorž
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. Kavšek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ž. Novak-Antolič
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. F. Jager
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to F. Jager.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fele-Žorž, G., Kavšek, G., Novak-Antolič, Ž. et al. 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 46, 911–922 (2008). https://doi.org/10.1007/s11517-008-0350-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-008-0350-y

Keywords

Search

Navigation