Early diagnosis of threatened premature labor by electrohysterographic recordings – The use of digital signal processing

• Autorzy: Lemancewicz A. , Borowska M. , Kuć P. , Jasińska E. , Laudański P. , Laudański T. , Oczeretko E.

Identyfikatory

DOI

10.1016/j.bbe.2015.11.005

• Języki publikacji: EN

Abstrakty

EN

Prevention and early diagnosis of imminent preterm labor are considered to be the most important perinatal challenge nowadays. Significant progress has been observed on postnatal care of premature infants, but without reducing the prevalence of preterm delivery. Our study was focused on comparison of three methods of spectral analysis of electro-hysterographic (EHG) signals: fast Fourier transform (FFT), wavelet transform (WT) and autoregressive modeling (AR). Complexity of the electrohysterographic signals was analyzed by using: the approximate entropy (ApEn), Lempel–Ziv complexity measure (L–Z). Additionally, the work evaluated the applicability of EHG in diagnosing imminent premature labor. EHG signals were recorded among 60 patients with threatened preterm labor symptoms between the 24th and 34th week of pregnancy. Patients included to the study had a shortened cervix (less than 20 mm) without regular uterine contractions recorded on regular cardiotocography (CTG). The women were divided into two groups: those delivering within 7 days – group A (n = 15) and women delivering after 7 days – group B (n = 45). The study confirmed differences in bioelectrical activity of uterus between patients delivering prematurely within 7 days and after from the EHG registration for all analyzed methods.

Słowa kluczowe

EN

uterine contraction; electrohysterography; preterm labor; spectral analysis; complexity measures

PL

skurcz macicy; elektrohisterografia; poród przedwczesny; analiza spektralna; miary złożoności

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2016
• Tom: Vol. 36, no. 1
• Strony: 302--307
• Opis fizyczny: Bibliogr. 35 poz., rys., tab., wykr.

Twórcy

autor

Lemancewicz A.

• Medical University of Bialystok, Faculty of Medicine, Department of Perinatology, Białystok, Poland

autor

Borowska M.

• Bialystok University of Technology, Faculty of Mechanical Engineering, Department of Materials and Biomedical Engineering, Białystok, Poland

autor

Kuć P.

• Medical University of Bialystok, Faculty of Medicine, Department of Perinatology, Białystok, Poland

autor

Jasińska E.

• Medical University of Bialystok, Faculty of Medicine, Department of Perinatology, Białystok, Poland

autor

Laudański P.

• Medical University of Bialystok, Faculty of Medicine, Department of Perinatology, Białystok, Poland

autor

Laudański T.

• Medical University of Bialystok, Faculty of Medicine, Department of Perinatology, Białystok, Poland

autor

Oczeretko E.

• Bialystok University of Technology, Faculty of Mechanical Engineering, Department of Materials and Biomedical Engineering, Wiejska 45C, 15-351 Białystok, Poland

Bibliografia

• [1] Goldenberg RL, Culhane JF, Iams JD, Romero R. Epidemiology and causes of preterm birth. Lancet 2008;371:75–84.
• [2] Kuc P, Laudanski P, Kowalczuk O, Chyczewski L, Laudanski T. Expression of selected genes in preterm premature rupture of fetal membranes. Acta Obstet Gynecol Scand 2012;91:936–43.
• [3] Kuc P, Laudanski P, Pierzynski P, Laudanski T. The effect of combined tocolysis on in vitro uterine contractility in preterm labour. Adv Med Sci 2011;56:88–94.
• [4] Kuc P, Lemancewicz A, Laudanski P, Kretowska M, Laudanski T. Total matrix metalloproteinase-8 serum levels in patients labouring preterm and patients with threatened preterm delivery. Folia Histochem Cytobiol 2010;48:366–70.
• [5] Laudanski P, Raba G, Kuc P, Lemancewicz A, Kisielewski R, Laudanski T. The assessment of the selected biochemical markers in predicting preterm labour. J Matern Fetal Neonatal Med 2012.
• [6] Laudanski P, Lemancewicz A, Pierzynski P, Akerlund M, Laudanski T. Decreased serum level of macrophage inflammatory chemokine-3beta/CCL19 in preterm labor and delivery. Eur J Obstet Gynecol Reprod Biol 2006;124:23–6.
• [7] Romero R, Mazaki-Tovi S, Vaisbuch E, Kusanovic JP, Chaiworapongsa T, Gomez R, et al. Metabolomics in premature labor: a novel approach to identify patients at risk for preterm delivery. J Matern Fetal Neonatal Med 2010;23:1344–59.
• [8] Gomez R, Romero R, Nien JK, Chaiworapongsa T, Medina L, Kim YM, et al. A short cervix in women with preterm labor and intact membranes: a risk factor for microbial invasion of the amniotic cavity. Am J Obstet Gynecol 2005;192: 678–89.
• [9] Romero R, Kalache KD, Kadar N. Timing the delivery of the preterm severely growth-restricted fetus: venous Doppler, cardiotocography or the biophysical profile? Ultrasound Obstet Gynecol Feb 2002;19:118–21.
• [10] Zietek J, Sikora J, Horoba K, Matonia A, Jezewski J, Magnucki J, et al. Mechanical and electrical uterine activity. Part I. Contractions monitoring. Ginekologia polska 2008;79:791–7.
• [11] Zietek J, Sikora J, Horoba K, Matonia A, Jezewski J, Magnucki J, et al. Mechanical and electrical uterine activity. Part II. Contractions parameters. Ginekologia polska 2008;79: 798–804.
• [12] Zietek J, Sikora J, Horoba K, Matonia A, Jezewski J, Magnucki J, et al. Prognostic value of chosen parameters of mechanical and bioelectrical uterine activity in prediction of threatening preterm labour. Ginekologia polska 2009;80:193–200.
• [13] Jezewski J, Horoba K, Matonia A, Wrobel J. Quantitative analysis of contraction patterns in electrical activity signal of pregnant uterus as an alternative to mechanical approach. Physiol Meas 2005;26:753.
• [14] Gupta D. Electrohysterography may be integral to remifentanil labor analgesia. J Matern Fetal Neonatal Med 2012;25:151–2.
• [15] Liapina Iu A, Nazarov SB, Nikanorov VN, Posiseeva LV, Nazarova AO. Regularity of changes in amplitude characteristics of electrohysterography of healthy pregnant women during third trimester. Fiziol Cheloveka 2011;37:100–3.
• [16] Reinhard J, Hayes-Gill BR, Schiermeier S, Loser H, Niedballa LM, Haarmann E, et al. Uterine activity monitoring during labour – a multi-centre, blinded two-way trial of external tocodynamometry against electrohysterography. Z Geburtshilfe Neonatol 2011;215:199–204.
• [17] Maul H, Maner W, Olson G, Saade G, Garfield R. Non-invasive transabdominal uterine electromyography correlates with the strength of intrauterine pressure and is predictive of labor and delivery. J Mater-Fet Neonatal Med 2004;15:297–301.
• [18] Jezewski J, Horoba K, Matonia A, Gacek A, Bernys M. A new approach to cardiotocographic fetal monitoring based on analysis of bioelectrical signals. Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE; 2003. p. 3145–8.
• [19] Muthuswamy J, Thakor NV. Spectral analysis methods for neurological signals. J Neurosci Meth 1998;83:1–14.
• [20] Fagard RH, Pardaens K, Staessen JA, Thijs L. Power spectral analysis of heart rate variability by autoregressive modeling and fast Fourier transform: a comparative study. Acta Cardiol 1998;53:211–8.
• [21] Challis RE, Kitney RI. Biomedical signal processing (in four parts). Part 3. The power spectrum and coherence function. Med Biol Eng Comput 1991;29:225–41.
• [22] Strambi SK, Rossi B, De Michele G, Sello S. Effect of medication in Parkinson's disease: a wavelet analysis of EMG signals. Med Eng Phys 2004;26:279–90.
• [23] Kronland-Martinet R, Morlet J, Grossman A. Analysis of sound patterns through wavelet transforms. Int J Pattern Recogn Artificial Intell 1987;69:273–301.
• [24] Box GEP, Jenkins GM, Reinsel GC. Time series analysis: forecasting and control; 1994.
• [25] Akaike H. A new look at the statistical model identification. IEEE Trans Aut Cont 1974;AC-19:716–23.
• [26] Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 1991;88:2297–301.
• [27] Fusheng Y, Bo H, Qingyu T. Approximate entropy and its application to biosignal analysis. In: Akay M, editor. Nonlinear biomedical signal processing: dynamic analysis and modeling. Hoboken, NJ, USA: John Wiley & Sons, Inc; 2000. p. 72–91.
• [28] Oczeretko E, Kitlas A, Świątecka J, Borowska M, Laudański T. Nonlinear dynamics in uterine contractions analysis. In: Losa GA, Merlini D, Nonnemacher TF, Weibel ER, editors. Fractals in biology and medicine, vol. IV. Basel: Birkhäuser Verlag; 2005. p. 215–22.
• [29] Lempel A, Ziv J. On the complexity of finite sequences. IEEE Trans Inform Theory 1976;22:75–81.
• [30] Szczepański J, Amigó JM, Wajnryb E, Sanchez-Vives MV. Characterizing spike trains with Lempel–Ziv complexity. Neurocomputing 2004;58:79–84.
• [31] Borowska M, Oczeretko E, Mazurek A, Kitlas A, Kuc P. Application of the Lempel–Ziv complexity measure to the analysis of biosignals and medical images. Annu Proc Med Sci 2005;50:29–30.
• [32] Euliano TY, Nguyen MT, Darmanjian S, McGorray SP, Euliano N, Onkala A. Monitoring uterine activity during labor: a comparison of 3 methods. Am J Obstet Gynecol 2013;208:66e1–6e.
• [33] Kerner R, Yogev Y, Belkin A, Ben-Haroush A, Zeevi B, Hod M. Maternal self-administered fetal heart rate monitoring and transmission from home in high-risk pregnancies. Int J Gynecol Obstet 2004;84.
• [34] Ungureanu GM, Gussi I, Wolf W, Taralunga D, Pasca S, Strungaru R. Prenatal telemedicine – advances in fetal monitoring. In: Graschew G, editor. Advances in Telemedicine: Applications in Various Medical Disciplines and Geographical Regions, InTech. 2011. pp. 97–120.
• [35] Lieto Di A, Campanile M, De Falco M, Carbone IF, Magenes G, Signorini MG, et al. Prenatal telemedicine: a new system for conventional and computerized telecardiotocography and tele-ultrasonography. In: Graschew G, editor. Advances in Telemedicine: Applications in Various Medical Disciplines and Geographical Regions, InTech. 2011. pp. 121–54.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.