Acoustic resonances in microfluidic chips: full-image micro-PIV experiments and numerical simulations

We show that full-image micro-PIV analysis in combination with images of transient particle motion is a powerful tool for experimental studies of acoustic radiation forces and acoustic streaming in microfluidic chambers under piezo-actuation in the MHz range. The measured steady-state motion of both large 5 um and small 1 um particles can be understood in terms of the acoustic eigenmodes or standing ultra-sound waves in the given experimental microsystems. This interpretation is supported by numerical solutions of the corresponding acoustic wave equation.Comment: RevTex, 10 pages, 9 eps figures; NOTE first authors changed his name to S. Melker Hagsater in the published versio

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

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 C37 weeks and pre-term delivery recordspregnancy 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 preterm 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 B 0.001)

A critical look at studies applying over-sampling on the TPEHGDB dataset

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set

The limits of inter-religious dialogue and the form of football rituals: The case of Bosnia-Herzegovina

The difficulties with interfaith dialogue are linked, at least in part, to the lack of ritual forms (consisting of rules, ceremonial idioms, liturgy, and repertoires of action) designed to unite and integrate the "meta-group "formed by the various religious communities. By means of ethnographic research conducted in Bosnia-Herzegovina, the author studied the mechanisms with which, under particular conditions, some forms of collective ritual were able to create opportunities for the re-integration of the Bosnian population, which had been profoundly divided after the terrible war of 199295. Comparing the forms of religious rituals and those of sports ritualsin particular, of football ritualsthe author develops some considerations that can be applied to the general debate about inter-religious dialogue. The comparison brings to light some of the limits and difficulties that religious institutions encounter in giving life to an interfaith dialogue that directly and concretely involves the members of different communities. © 2007 Social Compass

Identification of baryon resonances in central heavy-ion collisions at energies between 1 and 2 AGeV

The mass distributions of baryon resonances populated in near-central collisions of Au on Au and Ni on Ni are deduced by defolding the $p_t$ spectra of charged pions by a method which does not depend on a specific resonance shape. In addition the mass distributions of resonances are obtained from the invariant masses of $(p, \pi^{\pm})$ pairs. With both methods the deduced mass distributions are shifted by an average value of -60 MeV/c$^2$ relative to the mass distribution of the free $\Delta(1232)$ resonance, the distributions descent almost exponentially towards mass values of 2000 MeV/c^2. The observed differences between $(p, \pi^-)$ and $(p, \pi^+)$ pairs indicate a contribution of isospin $I = 1/2$ resonances. The attempt to consistently describe the deduced mass distributions and the reconstructed kinetic energy spectra of the resonances leads to new insights about the freeze out conditions, i.e. to rather low temperatures and large expansion velocities.Comment: 30 pages, 13 figures, Latex using documentstyle[12pt,a4,epsfig], to appear in Eur. Phys. J.

Feasibility and analysis of bipolar concentric recording of Electrohysterogram with flexible active electrode

The conduction velocity and propagation patterns of Electrohysterogram (EHG) provide fundamental information about uterine electrophysiological condition. The accuracy of these measurements can be impaired by both the poor spatial selectivity and sensitivity to the relative direction of the contraction propagation associated with conventional disc electrodes. Concentric ring electrodes could overcome these limitations the aim of this study was to examine the feasibility of picking up surface EHG signals using a new flexible tripolar concentric ring electrode (TCRE), and to compare it with conventional bipolar recordings. Simultaneous recording of conventional bipolar signals and bipolar concentric EHG (BC-EHG) were carried out on 22 pregnant women. Signal bursts were characterized and compared. No significant differences among channels in either duration or dominant frequency in the Fast Wave High frequency range were found. Nonetheless, the high pass filtering effect of the BC-EHG records resulted in lower frequency content within the range 0.1 to 0.2 Hz than the bipolar ones. Although the BC-EHG signal amplitude was about 5-7 times smaller than that of bipolar recordings, similar signal-to-noise ratio was obtained. These results suggest that the flexible TCRE is able to pick up uterine electrical activity and could provide additional information for deducing uterine electrophysiological condition.The authors are grateful to the Obstetrics Unit of the Hospital Universitario La Fe de Valencia (Valencia, Spain), where the recording sessions were carried out. The work was supported in part by the Ministerio de Ciencia y Tecnologia de Espana (TEC2010-16945), by the Universitat Politecnica de Valencia (PAID SP20120490) and Generalitat Valenciana (GV/2014/029) and by General Electric Healthcare.Ye Lin, Y.; Alberola Rubio, J.; Prats Boluda, G.; Perales Marin, AJ.; Desantes, D.; Garcia Casado, FJ. (2015). Feasibility and analysis of bipolar concentric recording of Electrohysterogram with flexible active electrode. Annals of Biomedical Engineering. 43(4):968-976. https://doi.org/10.1007/s10439-014-1130-5S968976434Alberola-Rubio, J., G. Prats-Boluda, Y. Ye-Lin, J. Valero, A. Perales, and J. Garcia-Casado. Comparison of non-invasive electrohysterographic recording techniques for monitoring uterine dynamics. Med. Eng. Phys. 35(12):1736–1743, 2013.Besio, W. G., K. Koka, R. Aakula, and W. Dai. Tri-polar concentric ring electrode development for laplacian electroencephalography. IEEE Trans. Biomed. Eng. 53(5):926–933, 2006.Devasahayam, S. R. Signals and Systems in Biomedical Engineering. Berlin: Springer, 2013.Devedeux, D., C. Marque, S. Mansour, G. Germain, and J. Duchene. Uterine electromyography: a critical review. Am. J. Obstet. Gynecol. 169(6):1636–1653, 1993.Estrada, L., A. Torres, J. Garcia-Casado, G. Prats-Boluda, and R. Jane. Characterization of laplacian surface electromyographic signals during isometric contraction in biceps brachii. Conf. Proc. IEEE Eng Med. Biol. Soc. 2013:535–538, 2013.Euliano, T. Y., D. Marossero, M. T. Nguyen, N. R. Euliano, J. Principe, and R. K. Edwards. Spatiotemporal electrohysterography patterns in normal and arrested labor. Am. J. Obstet. Gynecol. 200(1):54–57, 2009.Farina, D., and C. Cescon. Concentric-ring electrode systems for noninvasive detection of single motor unit activity. IEEE Trans. Biomed. Eng. 48(11):1326–1334, 2001.Fele-Zorz, G., 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. 46(9):911–922, 2008.Garfield, R. E., and W. L. Maner. Physiology and electrical activity of uterine contractions. Semin. Cell Dev. Biol. 18(3):289–295, 2007.Garfield, R. E., W. L. Maner, L. B. Mackay, D. Schlembach, and G. R. Saade. Comparing uterine electromyography activity of antepartum patients vs. term labor patients. Am. J. Obstet. Gynecol. 193(1):23–29, 2005.Garfield, R. E., H. Maul, L. Shi, W. Maner, C. Fittkow, G. Olsen, and G. R. Saade. Methods and devices for the management of term and preterm labor. Ann. N. Y. Acad. Sci. 943(1):203–224, 2001.Hassan, M., J. Terrien, C. Muszynski, A. Alexandersson, C. Marque, and B. Karlsson. Better pregnancy monitoring using nonlinear correlation analysis of external uterine electromyography. IEEE Trans. Biomed. Eng. 60(4):1160–1166, 2013.Kaufer, M., L. Rasquinha, and P. Tarjan. Optimization of multi-ring sensing electrode set, Conference proceedings of IEEE Engineering in Medicine and Biology Society, 1990, pp. 612–613.Koka, K., and W. G. Besio. Improvement of spatial selectivity and decrease of mutual information of tri-polar concentric ring electrodes. J. Neurosci. Methods 165(2):216–222, 2007.Lu, C.-C., and P. P. Tarjan. Pasteless, active, concentric ring sensors for directly obtained laplacian cardiac electrograms. J. Med. Biol. Eng. 22(4):199–203, 2002.Lucovnik, M., 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. Obstet. Gynecol. 204(3):228.e1–228.e10, 2011.Maner, W. L., and R. E. Garfield. Identification of human term and preterm labor using artificial neural networks on uterine electromyography data. Ann. Biomed. Eng. 35(3):465–473, 2007.Maner, W. L., R. E. Garfield, H. Maul, G. Olson, and G. Saade. Predicting term and preterm delivery with transabdominal uterine electromyography. Obstet. Gynecol. 101(6):1254–1260, 2003.Marque, C., J. M. Duchene, S. Leclercq, G. S. Panczer, and J. Chaumont. Uterine EHG processing for obstetrical monitoring. IEEE Trans. Biomed. Eng. 33(12):1182–1187, 1986.Marque, C. K., J. Terrien, S. Rihana, and G. Germain. Preterm labour detection by use of a biophysical marker: the uterine electrical activity. BMC. Pregnancy Childbirth. 7(Suppl1):S5, 2007.Maul, H., W. L. Maner, G. Olson, G. R. Saade, and R. E. Garfield. 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, 2004.Miles, A. M., M. Monga, and K. S. Richeson. Correlation of external and internal monitoring of uterine activity in a cohort of term patients. Am. J. Perinatol. 18(3):137–140, 2001.Prats-Boluda, G., J. Garcia-Casado, J. L. Martinez-de-Juan, and Y. Ye-Lin. Active concentric ring electrode for non-invasive detection of intestinal myoelectric signals. Med. Eng. Phys. 33(4):446–455, 2010.Prats-Boluda, G., Y. Ye-Lin, E. Garcia-Breijo, J. Ibañez, and J. Garcia-Casado. Active flexible concentric ring electrode for non-invasive surface bioelectrical recordings. Meas. Sci. Technol. 23(12):1–10, 2012.Rabotti, C., M. Mischi, S. G. Oei, and J. W. Bergmans. Noninvasive estimation of the electrohysterographic action-potential conduction velocity. IEEE Trans. Biomed. Eng. 57(9):2178–2187, 2010.Rabotti, C., S. G. Oei, H. J. van ‘t, and M. Mischi. Electrohysterographic propagation velocity for preterm delivery prediction. Am. J. Obstet. Gynecol. 205(6):e9–e10, 2011.Rooijakkers, M. J., S. Song, C. Rabotti, S. G. Oei, J. W. Bergmans, E. Cantatore, and M. Mischi. Influence of electrode placement on signal quality for ambulatory pregnancy monitoring. Comput. Math. Methods Med. 2014(1):960980, 2014.Schlembach, D., W. L. Maner, R. E. Garfield, and H. Maul. Monitoring the progress of pregnancy and labor using electromyography. Eur. J. Obstet. Gynecol. Reprod. Biol. 144(Suppl1):S33–S39, 2009.Sikora, J., A. Matonia, R. Czabanski, K. Horoba, J. Jezewski, and T. Kupka. Recognition of premature threatening labour symptoms from bioelectrical uterine activity signals. Arch. Perinatal Med. 17(2):97–103, 2011.Terrien, J., C. Marque, and B. Karlsson. Spectral characterization of human EHG frequency components based on the extraction and reconstruction of the ridges in the scalogram, Conference proceedings of IEEE Engineering in Medicine and Biology Society, 2007, pp. 1872–1875.Terrien, J., C. Marque, T. Steingrimsdottir, and B. Karlsson. Evaluation of adaptive filtering methods on a 16 electrode electrohysterogram recorded externally in labor, 11th Mediterranean Conference on Medical and Biomedical Engineering and Computing, 2007, Vol. 16, pp. 135–138.U.S. Preventive Services Task Force. Guide to clinical preventive services: an assessment of the effectiveness of 169 interventions. Baltimore: Willams & Wilkins, 1989

Characterization of the effects of Atosiban on uterine electromyograms recorded in women with threatened preterm labor

[EN] Although research studies using electrohysterography on women without tocolytic therapy have shown its potential for preterm birth diagnosis, tocolytics are usually administered in emergency rooms at the first sign of threatened preterm labor (TPL). Information on the uterine response during tocolytic treatment could prove useful for the development of tools able to predict true preterm deliveries under normal clinical conditions. The aim of this study was thus to analyze the effects of Atosiban on Electrohysterogram (EHG) parameters and to compare its effects on women who delivered preterm (WDP) and at term (WDT). Electrohysterograms recorded in different Atosiban therapy stages (before, during and after drug administration) on 40 WDT and 27 WDP were analyzed by computing linear, and non-linear EHG parameters. Results reveal that Atosiban does not greatly affect the EHG signal amplitude, but does modify its spectral content and reduces the energy associated with the fast wave high component in both WDP and WDT, with a faster response in the latter. EHG signal complexity remained constant in WDT, while it increased in WDP until it reached similar values to WDT during Atosiban treatment. The spectral and complexity parameters were able to separate (p < 0.05) WDT and WDP prior to and during tocolytic treatment and before and after treatment, respectively. The results pave the way for developing better and more reliable medical decision support systems based on EHG for preterm delivery prediction in TPL women in clinical scenarios.This work received financial support from the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (DPI2015-68397-R), VLC/Campus (UPV-FE-2018-B03) and by Conselleria de Educación, Investigación, Cultura y Deporte, Generalitat Valenciana (GV/2018/104).Mas-Cabo, J.; Prats-Boluda, G.; Ye Lin, Y.; Alberola Rubio, J.; Perales, A.; Garcia-Casado, J. (2019). Characterization of the effects of Atosiban on uterine electromyograms recorded in women with threatened preterm labor. Biomedical Signal Processing and Control. 52:198-205. https://doi.org/10.1016/j.bspc.2019.04.001S1982055

Prediction of Preterm Deliveries from EHG Signals Using Machine Learning

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier

Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment

[EN] As one of the main aims of obstetrics is to be able to detect imminent delivery in patients with threatened preterm labor, the techniques currently used in clinical practice have serious limitations in this respect. The electrohysterogram (EHG) has now emerged as an alternative technique, providing relevant information about labor onset when recorded in controlled checkups without administration of tocolytic drugs. The studies published to date mainly focus on EHG-burst analysis and, to a lesser extent, on whole EHG window analysis. The study described here assessed the ability of EHG signals to discriminate imminent labor (The ability of EHG recordings to predict imminent labor (<7days) was analyzed in preterm threatened patients undergoing tocolytic therapies by means of EHG-burst and whole EHG window analysis. The non-linear features were found to have better performance than the temporal and spectral parameters in separating women who delivered in less than 7days from those who did not.Mas-Cabo, J.; Prats-Boluda, G.; Perales Marín, AJ.; Garcia-Casado, J.; Alberola Rubio, J.; Ye Lin, Y. (2019). Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment. Medical & Biological Engineering & Computing. 57:401-411. https://doi.org/10.1007/s11517-018-1888-yS40141157Aboy M, Cuesta-Frau D, Austin D, Micó-Tormos P (2007) Characterization of sample entropy in the context of biomedical signal analysis. Conf Proc IEEE Eng Med Biol Soc:5942–5945. https://doi.org/10.1109/IEMBS.2007.4353701Aboy M, Hornero R, Abásolo D, Álvarez D (2006) Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis. IEEE Trans Biomed Eng 53:2282–2288. https://doi.org/10.1109/TBME.2006.883696Chkeir A, Fleury MJ, Karlsson B, Hassan M, Marque C (2013) Patterns of electrical activity synchronization in the pregnant rat uterus. Biomed 3:140–144. https://doi.org/10.1016/j.biomed.2013.04.007Crandon AJ (1979) Maternal anxiety and neonatal wellbeing. J Psychosom Res 23:113–115. https://doi.org/10.1016/0022-3999(79)90015-1Devedeux D, Marque C, Mansour S, Germain G, Duchêne J (1993) Uterine electromyography: a critical review. Am J Obstet Gynecol 169:1636–1653. https://doi.org/10.1016/0002-9378(93)90456-SFele-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. https://doi.org/10.1007/s11517-008-0350-yFergus P, Cheung P, Hussain A, al-Jumeily D, Dobbins C, Iram S (2013) Prediction of preterm deliveries from EHG signals using machine learning. PLoS One 8:e77154. https://doi.org/10.1371/journal.pone.0077154Garfield RE, Maner WL (2006) Biophysical methods of prediction and prevention of preterm labor: uterine electromyography and cervical light-induced fluorescence—new obstetrical diagnostic techniques. In: Preterm Birth pp 131–144Garfield RE, Maner WL (2007) Physiology and electrical activity of uterine contractions. Semin Cell Dev Biol 18:289–295. https://doi.org/10.1016/j.semcdb.2007.05.004Garfield RE, Maner WL, MacKay LB et al (2005) Comparing uterine electromyography activity of antepartum patients versus term labor patients. Am J Obstet Gynecol 193:23–29. https://doi.org/10.1016/j.ajog.2005.01.050Goldenberg RL, Culhane JF, Iams JD, Romero R (2008) Epidemiology and causes of preterm birth. Lancet 371:75–84. https://doi.org/10.1016/S0140-6736(08)60074-4American College of Obstetricians and Gynecologists and Committee on Practice Bulletins— Obstetrics (2012) Practice bulletin no. 127. Obstet Gynecol 119(6):1308–1317.Hadar E, Biron-Shental T, Gavish O, Raban O, Yogev Y (2015) A comparison between electrical uterine monitor, tocodynamometer and intra uterine pressure catheter for uterine activity in labor. J Matern Neonatal Med 28:1367–1374. https://doi.org/10.3109/14767058.2014.954539Hans P, Dewandre P, Brichant JF, Bonhomme V (2005) Comparative effects of ketamine on Bispectral Index and spectral entropy of the electroencephalogram under sevoflurane anaesthesia. Br J Anaesth 94:336–340. https://doi.org/10.1093/bja/aei047Hassan 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. https://doi.org/10.1016/j.medengphy.2011.03.010Hassan M, Terrien J, Muszynski C et al (2013) Better pregnancy monitoring using nonlinear correlation analysis of external uterine electromyography. IEEE Trans Biomed Eng 60:1160–1166. https://doi.org/10.1109/TBME.2012.2229279Horoba K, Jezewski J, Matonia A, Wrobel J, Czabanski R, Jezewski M (2016) Early predicting a risk of preterm labour by analysis of antepartum electrohysterograhic signals. Biocybern Biomed Eng 36:574–583. https://doi.org/10.1016/j.bbe.2016.06.004Lawn JE, Wilczynska-Ketende K, Cousens SN (2006) Estimating the causes of 4 million neonatal deaths in the year 2000. Int J Epidemiol 35:706–718. https://doi.org/10.1093/ije/dyl043Lemancewicz A, Borowska M, Kuć P, Jasińska E, Laudański P, Laudański T, Oczeretko E (2016) Early diagnosis of threatened premature labor by electrohysterographic recordings—the use of digital signal processing. Biocybern Biomed Eng 36:302–307. https://doi.org/10.1016/j.bbe.2015.11.005M L WLM, LR C (2012) Noninvasive uterine electromyography for prediction of preterm delivery. Am J Obstet Gynecol 204:1–20. https://doi.org/10.1016/j.ajog.2010.09.024.NoninvasiveManer WL, Garfield RE (2007) Identification of human term and preterm labor using artificial neural networks on uterine electromyography data. Ann Biomed Eng 35:465–473. https://doi.org/10.1007/s10439-006-9248-8Maner WL, Garfield RE, Maul H, Olson G, Saade G (2003) Predicting term and preterm delivery with transabdominal uterine electromyography. Obstet Gynecol 101:1254–1260. https://doi.org/10.1016/S0029-7844(03)00341-7Marque C, Gondry J (1999) Use of the electrohysterogram signal for characterization of contractions during pregnancy. IEEE Trans Biomed Eng 46:1222–1229Maul 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 15:297–301Mischi M, Chen C, Ignatenko T, de Lau H, Ding B, Oei SGG, Rabotti C (2018) Dedicated entropy measures for early assessment of pregnancy progression from single-channel electrohysterography. IEEE Trans Biomed Eng 65:875–884. https://doi.org/10.1109/TBME.2017.2723933Most O, Langer O, Kerner R, Ben David G, Calderon I (2008) Can myometrial electrical activity identify patients in preterm labor? Am J Obstet Gynecol 199:378. https://doi.org/10.1016/j.ajog.2008.08.003Petrou S (2005) The economic consequences of preterm birth during the first 10 years of life. BJOG 112:10–15. https://doi.org/10.1111/j.1471-0528.2005.00577.xRabotti C, Sammali F, Kuijsters N, et al (2015) Analysis of uterine activity in nonpregnant women by electrohysterography: a feasibility study. In: Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS pp 5916–5919Schlembach D, Maner WL, Garfield RE, Maul H (2009) Monitoring the progress of pregnancy and labor using electromyography. Eur J Obstet Gynecol Reprod Biol 144:2–8. https://doi.org/10.1016/j.ejogrb.2009.02.016Sikora J, Matonia A, Czabański R et al (2011) Recognition of premature threatening labour symptoms from bioelectrical uterine activity signals. Arch Perinat Med 17:97–103Vinken MPGC, Rabotti C, Mischi M, van Laar JOEH, Oei SG (2010) Nifedipine-induced changes in the electrohysterogram of preterm contractions: feasibility in clinical practice. Obstet Gynecol Int 2010:325635. https://doi.org/10.1155/2010/325635Vrhovec J, Lebar AM (2012) An uterine electromyographic activity as a measure of labor progression. Appl EMG Clin Sport Med 243–268. doi: https://doi.org/10.5772/25526Vrhovec J, Macek-Lebar A, Rudel D (2007) Evaluating uterine electrohysterogram with entropy. 11th Mediterr Conf Med Biomed Eng Comput 144–147. https://doi.org/10.1007/978-3-540-73044-6_36Ye-Lin Y, Bueno-Barrachina JM, Prats-boluda G, Rodriguez de Sanabria R, Garcia-Casado J (2017) Wireless sensor node for non-invasive high precision electrocardiographic signal acquisition based on a multi-ring electrode. Measurement 97:195–202. https://doi.org/10.1016/J.MEASUREMENT.2016.11.009Ye-Lin Y, Garcia-Casado J, Prats-Boluda G, Alberola-Rubio J, Perales A (2014) Automatic identification of motion artifacts in EHG recording for robust analysis of uterine contractions. Comput Math Methods Med 2014:1–11. https://doi.org/10.1155/2014/470786Zhang XS, Roy RJ, Jensen EW (2001) EEG complexity as a measure of depth of anesthesia for patients. IEEE Trans Biomed Eng 48:1424–1433. https://doi.org/10.1109/10.96660