Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography

[EN] Preterm birth is the leading cause of death in newborns and the survivors are prone to health complications. Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy. The current methods used in clinical practice to diagnose preterm labor, the Bishop score or cervical length, have high negative predictive values but not positive ones. In this work we analyzed the performance of computationally efficient classification algorithms, based on electrohysterographic recordings (EHG), such as random forest (RF), extreme learning machine (ELM) and K-nearest neighbors (KNN) for imminent labor (<7 days) prediction in women with TPL, using the 50th or 10th-90th percentiles of temporal, spectral and nonlinear EHG parameters with and without obstetric data inputs. Two criteria were assessed for the classifier design: F1-score and sensitivity. RFF1_2 and ELMF1_2 provided the highest F1-score values in the validation dataset, (88.17 +/- 8.34% and 90.2 +/- 4.43%) with the 50th percentile of EHG and obstetric inputs. ELMF1_2 outperformed RFF1_2 in sensitivity, being similar to those of ELMSens (sensitivity optimization). The 10th-90th percentiles did not provide a significant improvement over the 50th percentile. KNN performance was highly sensitive to the input dataset, with a high generalization capability.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR); by the Generalitat Valenciana (AICO/2019/220).Prats-Boluda, G.; Pastor-Tronch, J.; Garcia-Casado, J.; Monfort-Ortiz, R.; Perales Marín, A.; Diago, V.; Roca Prats, A.... (2021). Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography. Sensors. 21(7):1-18. https://doi.org/10.3390/s21072496S11821

Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios

[EN] Electrohysterography (EHG) has been shown to provide relevant information on uterine activity and could be used for predicting preterm labor and identifying other maternal fetal risks. The extraction of high-quality robust features is a key factor in achieving satisfactory prediction systems from EHG. Temporal, spectral, and non-linear EHG parameters have been computed to characterize EHG signals, sometimes obtaining controversial results, especially for non-linear parameters. The goal of this work was to assess the performance of EHG parameters in identifying those robust enough for uterine electrophysiological characterization. EHG signals were picked up in different obstetric scenarios: antepartum, including women who delivered on term, labor, and post-partum. The results revealed that the 10th and 90th percentiles, for parameters with falling and rising trends as labor approaches, respectively, differentiate between these obstetric scenarios better than median analysis window values. Root-mean-square amplitude, spectral decile 3, and spectral moment ratio showed consistent tendencies for the different obstetric scenarios as well as non-linear parameters: Lempel-Ziv, sample entropy, spectral entropy, and SD1/SD2 when computed in the fast wave high bandwidth. These findings would make it possible to extract high quality and robust EHG features to improve computer-aided assessment tools for pregnancy, labor, and postpartum progress and identify maternal fetal risks.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR) and the Generalitat Valenciana (AICO/2019/220 & GV/2018/104)Mas-Cabo, J.; Ye Lin, Y.; Garcia-Casado, J.; Díaz-Martínez, MDA.; Perales-Marin, A.; Monfort-Ortiz, R.; Roca-Prats, A.... (2020). Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios. Entropy. 22(7):1-15. https://doi.org/10.3390/e22070743S115227Wagura, P., Wasunna, A., Laving, A., Wamalwa, D., & Ng’ang’a, P. (2018). Prevalence and factors associated with preterm birth at kenyatta national hospital. BMC Pregnancy and Childbirth, 18(1). doi:10.1186/s12884-018-1740-2Liu, L., Johnson, H. L., Cousens, S., Perin, J., Scott, S., Lawn, J. E., … Black, R. E. (2012). Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. The Lancet, 379(9832), 2151-2161. doi:10.1016/s0140-6736(12)60560-1Howson, C. P., Kinney, M. V., McDougall, L., & Lawn, J. E. (2013). Born Too Soon: Preterm birth matters. Reproductive Health, 10(S1). doi:10.1186/1742-4755-10-s1-s1Euliano, T. Y., Nguyen, M. T., Darmanjian, S., McGorray, S. P., Euliano, N., Onkala, A., & Gregg, A. R. (2013). Monitoring uterine activity during labor: a comparison of 3 methods. American Journal of Obstetrics and Gynecology, 208(1), 66.e1-66.e6. doi:10.1016/j.ajog.2012.10.873Devedeux, D., Marque, C., Mansour, S., Germain, G., & Duchêne, J. (1993). Uterine electromyography: A critical review. American Journal of Obstetrics and Gynecology, 169(6), 1636-1653. doi:10.1016/0002-9378(93)90456-sChkeir, A., Fleury, M.-J., Karlsson, B., Hassan, M., & Marque, C. (2013). Patterns of electrical activity synchronization in the pregnant rat uterus. BioMedicine, 3(3), 140-144. doi:10.1016/j.biomed.2013.04.007Fele-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. Medical & Biological Engineering & Computing, 46(9), 911-922. doi:10.1007/s11517-008-0350-yMas-Cabo, J., Prats-Boluda, G., Perales, A., Garcia-Casado, J., Alberola-Rubio, J., & Ye-Lin, Y. (2018). Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment. Medical & Biological Engineering & Computing, 57(2), 401-411. doi:10.1007/s11517-018-1888-yVinken, M. P. G. C., Rabotti, C., Mischi, M., & Oei, S. G. (2009). Accuracy of Frequency-Related Parameters of the Electrohysterogram for Predicting Preterm Delivery. Obstetrical & Gynecological Survey, 64(8), 529-541. doi:10.1097/ogx.0b013e3181a8c6b1Hassan, M., Terrien, J., Marque, C., & Karlsson, B. (2011). Comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals. Medical Engineering & Physics, 33(8), 980-986. doi:10.1016/j.medengphy.2011.03.010Lemancewicz, 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. Biocybernetics and Biomedical Engineering, 36(1), 302-307. doi:10.1016/j.bbe.2015.11.005Garcia-Casado, J., Ye-Lin, Y., Prats-Boluda, G., Mas-Cabo, J., Alberola-Rubio, J., & Perales, A. (2018). Electrohysterography in the diagnosis of preterm birth: a review. Physiological Measurement, 39(2), 02TR01. doi:10.1088/1361-6579/aaad56Most, O., Langer, O., Kerner, R., Ben David, G., & Calderon, I. (2008). Can myometrial electrical activity identify patients in preterm labor? American Journal of Obstetrics and Gynecology, 199(4), 378.e1-378.e6. doi:10.1016/j.ajog.2008.08.003Verdenik, I., Pajntar, M., & Leskošek, B. (2001). Uterine electrical activity as predictor of preterm birth in women with preterm contractions. European Journal of Obstetrics & Gynecology and Reproductive Biology, 95(2), 149-153. doi:10.1016/s0301-2115(00)00418-8Horoba, 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. Biocybernetics and Biomedical Engineering, 36(4), 574-583. doi:10.1016/j.bbe.2016.06.004Lucovnik, M., Maner, W. L., Chambliss, L. R., Blumrick, R., Balducci, J., Novak-Antolic, Z., & Garfield, R. E. (2011). Noninvasive uterine electromyography for prediction of preterm delivery. American Journal of Obstetrics and Gynecology, 204(3), 228.e1-228.e10. doi:10.1016/j.ajog.2010.09.024Smrdel, A., & Jager, F. (2015). Separating sets of term and pre-term uterine EMG records. Physiological Measurement, 36(2), 341-355. doi:10.1088/0967-3334/36/2/341Maner, W. (2003). Predicting term and preterm delivery with transabdominal uterine electromyography. Obstetrics & Gynecology, 101(6), 1254-1260. doi:10.1016/s0029-7844(03)00341-7Leman, H., Marque, C., & Gondry, J. (1999). Use of the electrohysterogram signal for characterization of contractions during pregnancy. IEEE Transactions on Biomedical Engineering, 46(10), 1222-1229. doi:10.1109/10.790499Mischi, M., Chen, C., Ignatenko, T., de Lau, H., Ding, B., Oei, S. G. G., & Rabotti, C. (2018). Dedicated Entropy Measures for Early Assessment of Pregnancy Progression From Single-Channel Electrohysterography. IEEE Transactions on Biomedical Engineering, 65(4), 875-884. doi:10.1109/tbme.2017.2723933Garfield, R. E., Maner, W. L., MacKay, L. B., Schlembach, D., & Saade, G. R. (2005). Comparing uterine electromyography activity of antepartum patients versus term labor patients. American Journal of Obstetrics and Gynecology, 193(1), 23-29. doi:10.1016/j.ajog.2005.01.050Maner, W. L., & Garfield, R. E. (2007). Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data. Annals of Biomedical Engineering, 35(3), 465-473. doi:10.1007/s10439-006-9248-8DIMITROV, G. V., ARABADZHIEV, T. I., MILEVA, K. N., BOWTELL, J. L., CRICHTON, N., & DIMITROVA, N. A. (2006). Muscle Fatigue during Dynamic Contractions Assessed by New Spectral Indices. Medicine & Science in Sports & Exercise, 38(11), 1971-1979. doi:10.1249/01.mss.0000233794.31659.6dNagarajan, R., Eswaran, H., Wilson, J. D., Murphy, P., Lowery, C., & Preißl, H. (2003). Analysis of uterine contractions: a dynamical approach. The Journal of Maternal-Fetal & Neonatal Medicine, 14(1), 8-21. doi:10.1080/jmf.14.1.8.21Zhang, X.-S., Roy, R. J., & Jensen, E. W. (2001). EEG complexity as a measure of depth of anesthesia for patients. IEEE Transactions on Biomedical Engineering, 48(12), 1424-1433. doi:10.1109/10.966601Garfield, R. E., Maner, W. L., Maul, H., & Saade, G. R. (2005). Use of uterine EMG and cervical LIF in monitoring pregnant patients. BJOG: An International Journal of Obstetrics & Gynaecology, 112, 103-108. doi:10.1111/j.1471-0528.2005.00596.xGrotegut, C. A., Paglia, M. J., Johnson, L. N. C., Thames, B., & James, A. H. (2011). Oxytocin exposure during labor among women with postpartum hemorrhage secondary to uterine atony. American Journal of Obstetrics and Gynecology, 204(1), 56.e1-56.e6. doi:10.1016/j.ajog.2010.08.02

Neuromodulation-induced prehabilitation to leverage neuroplasticity before brain tumor surgery: a single-cohort feasibility trial protocol

IntroductionNeurosurgery for brain tumors needs to find a complex balance between the effective removal of targeted tissue and the preservation of surrounding brain areas. Neuromodulation-induced cortical prehabilitation (NICP) is a promising strategy that combines temporary inhibition of critical areas (virtual lesion) with intensive behavioral training to foster the activation of alternative brain resources. By progressively reducing the functional relevance of targeted areas, the goal is to facilitate resection with reduced risks of neurological sequelae. However, it is still unclear which modality (invasive vs. non-invasive neuromodulation) and volume of therapy (behavioral training) may be optimal in terms of feasibility and efficacy.Methods and analysisPatients undertake between 10 and 20 daily sessions consisting of neuromodulation coupled with intensive task training, individualized based on the target site and neurological functions at risk of being compromised. The primary outcome of the proposed pilot, single-cohort trial is to investigate the feasibility and potential effectiveness of a non-invasive NICP protocol on neuroplasticity and post-surgical outcomes. Secondary outcomes investigating longitudinal changes (neuroimaging, neurophysiology, and clinical) are measured pre-NICP, post-NICP, and post-surgery.Ethics and disseminationEthics approval was obtained from the Research Ethical Committee of Fundació Unió Catalana d'Hospitals (approval number: CEI 21/65, version 1, 13/07/2021). The results of the study will be submitted to a peer-reviewed journal and presented at scientific congresses.Clinical trial registrationClinicalTrials.gov, identifier NCT05844605

Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios

Electrohysterography (EHG) has been shown to provide relevant information on uterine activity and could be used for predicting preterm labor and identifying other maternal fetal risks. The extraction of high-quality robust features is a key factor in achieving satisfactory prediction systems from EHG. Temporal, spectral, and non-linear EHG parameters have been computed to characterize EHG signals, sometimes obtaining controversial results, especially for non-linear parameters. The goal of this work was to assess the performance of EHG parameters in identifying those robust enough for uterine electrophysiological characterization. EHG signals were picked up in different obstetric scenarios: antepartum, including women who delivered on term, labor, and post-partum. The results revealed that the 10th and 90th percentiles, for parameters with falling and rising trends as labor approaches, respectively, differentiate between these obstetric scenarios better than median analysis window values. Root-mean-square amplitude, spectral decile 3, and spectral moment ratio showed consistent tendencies for the different obstetric scenarios as well as non-linear parameters: Lempel&ndash;Ziv, sample entropy, spectral entropy, and SD1/SD2 when computed in the fast wave high bandwidth. These findings would make it possible to extract high quality and robust EHG features to improve computer-aided assessment tools for pregnancy, labor, and postpartum progress and identify maternal fetal risks

Neuromodulation-induced prehabilitation to leverage neuroplasticity before brain tumor surgery: a single-cohort feasibility trial protocol

Introduction: Neurosurgery for brain tumors needs to find a complex balance between the effective removal of targeted tissue and the preservation of surrounding brain areas. Neuromodulation-induced cortical prehabilitation (NICP) is a promising strategy that combines temporary inhibition of critical areas (virtual lesion) with intensive behavioral training to foster the activation of alternative brain resources. By progressively reducing the functional relevance of targeted areas, the goal is to facilitate resection with reduced risks of neurological sequelae. However, it is still unclear which modality (invasive vs. non-invasive neuromodulation) and volume of therapy (behavioral training) may be optimal in terms of feasibility and efficacy.Methods and analysis: Patients undertake between 10 and 20 daily sessions consisting of neuromodulation coupled with intensive task training, individualized based on the target site and neurological functions at risk of being compromised. The primary outcome of the proposed pilot, single-cohort trial is to investigate the feasibility and potential effectiveness of a non-invasive NICP protocol on neuroplasticity and post-surgical outcomes. Secondary outcomes investigating longitudinal changes (neuroimaging, neurophysiology, and clinical) are measured pre-NICP, post-NICP, and post-surgery.Ethics and dissemination: Ethics approval was obtained from the Research Ethical Committee of Fundacio Unio Catalana d'Hospitals (approval number: CEI 21/65, version 1, 13/07/2021). The results of the study will be submitted to a peer-reviewed journal and presented at scientific congresses