Skip to main content
SpringerLink
Log in

Recurring patterns in stationary intervals of abdominal uterine electromyograms during gestation

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

Abstract

Abdominal uterine electromyograms (uEMG) studies have focused on uterine contractions to describe the evolution of uterine activity and preterm birth (PTB) prediction. Stationary, non-contracting uEMG has not been studied. The aim of the study was to investigate the recurring patterns in stationary uEMG, their relationship with gestation age and PTB, and PTB predictivity. A public database of 300 (38 PTB) three-channel (S1–S3) uEMG recordings of 30 min, collected between 22 and 35 weeks’ gestation, was used. Motion and labour contraction-free intervals in uEMG were identified as 5-min weak-sense stationarity intervals in 268 (34 PTB) recordings. Sample entropy (SampEn), percentage recurrence (PR), percentage determinism (PD), entropy (ER), and maximum length (L MAX) of recurrence were calculated and analysed according to the time to delivery and PTB. Random time series were generated by random shuffle (RS) of actual data. Recurrence was present in actual data (p < 0.001) but not RS. In S3, PR (p < 0.005), PD (p < 0.01), ER (p < 0.005), and L MAX (p < 0.05) were higher, and SampEn lower (p < 0.005) in PTB. Recurrence indices increased (all p < 0.001) and SampEn decreased (p < 0.01) with decreasing time to delivery, suggesting increasingly regular and recurring patterns with gestation progression. All indices predicted PTB with AUC ≥0.62 (p < 0.05). Recurring patterns in stationary non-contracting uEMG were associated with time to delivery but were relatively poor predictors of PTB.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Censi F, Barbaro V, Bartolini P, Calcagnini G, Michelucci A, Gensini GF, Cerutti S (2000) Recurrent patterns of atrial depolarization during atrial fibrillation assessed by recurrence plot quantification. Ann Biomed Eng 28:61–70

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  3. Eckmann JP, Kamphorst SO, Ruelle D (1987) Recurrence plots of dynamical systems. Europhys Lett 4:973–977

    Article  Google Scholar 

  4. Fele-Žorž G, Kavšek G, Novak-Antolič Ž, Jager F (2008) 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

    Article  PubMed  Google Scholar 

  5. Garfield RE, Saade G, Buhimschi C, Buhimschi I, Shi L, Shi SQ, Chwalisz K (1998) Control and assessment of the uterus and cervix during pregnancy and labour. Hum Reprod Update 4:673–695

    Article  CAS  PubMed  Google Scholar 

  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 193:23–29

    Article  PubMed  Google Scholar 

  7. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng CK, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101:e215–e220

    Article  CAS  PubMed  Google Scholar 

  8. Grgic O, Matijevic R, Kuna K (2012) Raised electrical uterine activity and shortened cervical length could predict preterm delivery in a low-risk population. Arch Gynecol Obstet 285:31–35

    Article  PubMed  Google Scholar 

  9. Hassan M, Alexandersson A, Terrien J, Muszynski C, Marque C, Karlsson B (2013) Better pregnancy monitoring using nonlinear propagation analysis of external uterine electromyography. IEEE Trans Biomed Eng 60:1160–1166

    Article  PubMed  Google Scholar 

  10. Hassan M, Terrien J, Marque C, Karlsson B (2011) Comparison between approximate entropy, correntropy and time reversibility: application to uterine electromyogram signals. Med Eng Phys 33:980–986

    Article  PubMed  Google Scholar 

  11. Hausdorff JM (2009) Gait dynamics in Parkinson’s disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling. Chaos 19:026113

    Article  PubMed Central  PubMed  Google Scholar 

  12. Kennel MB, Brown R, Abarbanel HD (1992) Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A 45:3403–3411

    Article  PubMed  Google Scholar 

  13. Lucovnik M, Maner WL, Chambliss LR, Blumrick R, Balducci J, Novak-Antolic Z, Garfield RE (2011) Noninvasive uterine electromyography for prediction of preterm delivery. Am J Obstet Gynecol 204(228):e1–e10

    PubMed  Google Scholar 

  14. Magagnin V, Bassani T, Bari V, Turiel M, Maestri R, Pinna GD, Porta A (2011) Non-stationarities significantly distort short-term spectral, symbolic and entropy heart rate variability indices. Physiol Meas 32:1775–1786

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  17. Marque CK, Terrien J, Rihana S, Germain G (2007) Preterm labour detection by use of a biophysical marker: the uterine electrical activity. BMC Pregnancy Childbirth 7(Suppl 1):S5

    Article  PubMed Central  PubMed  Google Scholar 

  18. Mohebbi M, Ghassemian H (2011) Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal. Physiol Meas 32:1147–1162

    Article  PubMed  Google Scholar 

  19. Most O, Langer O, Kerner R, David GB, Calderon I (2008) Can myometrial electrical activity identify patients in preterm labor? Am J Obstet Gynecol 199(378):e1–e6

    PubMed  Google Scholar 

  20. Porta A, D’Addio G, Guzzetti S, Lucini D, Pagani M (2004) Testing the presence of non stationarities in short heart rate variability series. Comp Cardiol 31:645–648

    Google Scholar 

  21. Rabotti C, Mischi M, Oei SG, Bergmans JW (2010) Noninvasive estimation of the electrohysterographic action-potential conduction velocity. IEEE Trans Biomed Eng 57:2178–2187

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  23. Webber CL Jr (2012) Recurrence quantification of fractal structures. Front Physiol 3:382

    Article  PubMed Central  PubMed  Google Scholar 

  24. Webber CL Jr, Zbilut JP (1994) Dynamical assessment of physiological systems and states using recurrence plot strategies. J Appl Physiol 76:965–973

    PubMed  Google Scholar 

Download references

Acknowledgments

PL is funded by the National Institute for Health Research Newcastle Biomedical Research Centre based at Newcastle Hospitals Foundation Trust and Newcastle University. CM is supported by Research Capacity Funding from Newcastle upon Tyne NHS Foundation Trust. WT is funded by a Medical Research Council Bioinformatics Training Fellowship (Grant No. G902091).

Author information

Authors and Affiliations

  1. Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE1 4LP, UK

    Luigi Yuri Di Marco, Wing-Chiu Tong, Michael J. Taggart, Stephen C. Robson & Philip Langley

  2. Center for Computational Imaging & Simulation Technologies in Biomedicine (CISTIB), Department of Mechanical Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Luigi Yuri Di Marco

  3. Regional Medical Physics Department, Royal Victoria Infirmary, Queen Victoria Road, Newcastle upon Tyne, NE1 4LP, UK

    Costanzo Di Maria

  4. School of Engineering, University of Hull, Hull, HU6 7RX, UK

    Philip Langley

Authors
  1. Luigi Yuri Di Marco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Costanzo Di Maria
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wing-Chiu Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael J. Taggart
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stephen C. Robson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Philip Langley
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luigi Yuri Di Marco.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Di Marco, L.Y., Di Maria, C., Tong, WC. et al. Recurring patterns in stationary intervals of abdominal uterine electromyograms during gestation. Med Biol Eng Comput 52, 707–716 (2014). https://doi.org/10.1007/s11517-014-1174-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-014-1174-6

Keywords

Search

Navigation