Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records

[EN] Preterm labor is one of the major causes of neonatal deaths and also the cause of significant health and development impairments in those who survive. However, there are still no reliable and accurate tools for preterm labor prediction in clinical settings. Electrohysterography (EHG) has been proven to provide relevant information on the labor time horizon. Many studies focused on predicting preterm labor by using temporal, spectral, and nonlinear parameters extracted from single EHG recordings. However, multichannel analysis, which includes information from the whole uterus and about coupling between the recording areas, may provide better results. The cross validation method is often used to design classifiers and evaluate their performance. However, when the validation dataset is used to tune the classifier hyperparameters, the performance metrics of this dataset may not properly assess its generalization capacity. In this work, we developed and compared different classifiers, based on artificial neural networks, for predicting preterm labor using EHG features from single and multichannel recordings. A set of temporal, spectral, nonlinear, and synchronization parameters computed from EHG recordings was used as the input features. All the classifiers were evaluated on independent test datasets, which were never ¿seen¿ by the models, to determine their generalization capacity. Classifiers¿ performance was also evaluated when obstetrical data were included. The experimental results show that the classifier performance metrics were significantly lower in the test dataset (AUC range 76-91%) than in the train and validation sets (AUC range 90-99%). The multichannel classifiers outperformed the single-channel classifiers, especially when information was combined into mean efficiency indexes and included coupling information between channels. Including obstetrical data slightly improved the classifier metrics and reached an AUC of for the test dataset. These results show promise for the transfer of the EHG technique to preterm labor prediction in clinical practice.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (DPI2015-68397-R, MINECO/FEDER, and RTI2018-094449-A-I00-AR); Generalitat Valenciana (AICO/2019/220); and the VLC/Campus (UPV-FE-2018-B03).Mas-Cabo, J.; Prats-Boluda, G.; Garcia-Casado, J.; Alberola Rubio, J.; Perales Marín, AJ.; Ye Lin, Y. (2019). Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records. Journal of Sensors. 2019:1-13. https://doi.org/10.1155/2019/5373810S1132019Goldenberg, R. L., Culhane, J. F., Iams, J. D., & Romero, R. (2008). Epidemiology and causes of preterm birth. 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BJOG: An International Journal of Obstetrics & Gynaecology, 112, 103-108. doi:10.1111/j.1471-0528.2005.00596.xDevedeux, 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.2013.04.007Fuchs, A.-R., Fuchs, F., Husslein, P., & Soloff, M. S. (1984). Oxytocin receptors in the human uterus during pregnancy and parturition. American Journal of Obstetrics and Gynecology, 150(6), 734-741. doi:10.1016/0002-9378(84)90677-xTezuka, N., Ali, M., Chwalisz, K., & Garfield, R. E. (1995). Changes in transcripts encoding calcium channel subunits of rat myometrium during pregnancy. 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Medical & Biological Engineering & Computing, 46(9), 911-922. doi:10.1007/s11517-008-0350-yDiab, A., Hassan, M., Marque, C., & Karlsson, B. (2014). Performance analysis of four nonlinearity analysis methods using a model with variable complexity and application to uterine EMG signals. Medical Engineering & Physics, 36(6), 761-767. doi:10.1016/j.medengphy.2014.01.009Lucovnik, 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.2010.09.024Acharya, U. R., Sudarshan, V. K., Rong, S. Q., Tan, Z., Lim, C. M., Koh, J. E., … Bhandary, S. V. (2017). Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals. Computers in Biology and Medicine, 85, 33-42. doi:10.1016/j.compbiomed.2017.04.013Fergus, P., Idowu, I., Hussain, A., & Dobbins, C. (2016). Advanced artificial neural network classification for detecting preterm births using EHG records. Neurocomputing, 188, 42-49. doi:10.1016/j.neucom.2015.01.107Ren, P., Yao, S., Li, J., Valdes-Sosa, P. A., & Kendrick, K. M. (2015). Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals. PLOS ONE, 10(7), e0132116. doi:10.1371/journal.pone.0132116Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145-1159. doi:10.1016/s0031-3203(96)00142-2Maner, 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/s10439-006-9248-8Smrdel, A., & Jager, F. (2015). Separating sets of term and pre-term uterine EMG records. Physiological Measurement, 36(2), 341-355. doi:10.1088/0967-3334/36/2/341Aditya, S., & Tibarewala, D. N. (2012). Comparing ANN, LDA, QDA, KNN and SVM algorithms in classifying relaxed and stressful mental state from two-channel prefrontal EEG data. International Journal of Artificial Intelligence and Soft Computing, 3(2), 143. doi:10.1504/ijaisc.2012.049010Li, Q., Rajagopalan, C., & Clifford, G. D. (2014). A machine learning approach to multi-level ECG signal quality classification. Computer Methods and Programs in Biomedicine, 117(3), 435-447. doi:10.1016/j.cmpb.2014.09.002Li, Z., Zhang, Q., & Zhao, X. (2017). Performance analysis of K-nearest neighbor, support vector machine, and artificial neural network classifiers for driver drowsiness detection with different road geometries. International Journal of Distributed Sensor Networks, 13(9), 155014771773339. doi:10.1177/1550147717733391Murthy, H. S. N., & Meenakshi, D. M. (2015). ANN, SVM and KNN Classifiers for Prognosis of Cardiac Ischemia- A Comparison. Bonfring International Journal of Research in Communication Engineering, 5(2), 07-11. doi:10.9756/bijrce.8030Ren, J. (2012). ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging. Knowledge-Based Systems, 26, 144-153. doi:10.1016/j.knosys.2011.07.016Maul, H., Maner, W., Olson, G., Saade, G., & Garfield, R. (2004). Non-invasive transabdominal uterine electromyography correlates with the strength of intrauterine pressure and is predictive of labor and delivery. The Journal of Maternal-Fetal & Neonatal Medicine, 15(5), 297-301. doi:10.1080/14767050410001695301Mas-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. 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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

Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy

[EN] Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy and entails high costs for health systems. Currently, no reliable labor proximity prediction techniques are available for clinical use. Regular checks by uterine electrohysterogram (EHG) for predicting preterm labor have been widely studied. The aim of the present study was to assess the feasibility of predicting labor with a 7- and 14-day time horizon in TPL women, who may be under tocolytic treatment, using EHG and/or obstetric data. Based on 140 EHG recordings, artificial neural networks were used to develop prediction models. Non-linear EHG parameters were found to be more reliable than linear for differentiating labor in under and over 7/14 days. Using EHG and obstetric data, the <7- and <14-day labor prediction models achieved an AUC in the test group of 87.1 +/- 4.3% and 76.2 +/- 5.8%, respectively. These results suggest that EHG can be reliable for predicting imminent labor in TPL women, regardless of the tocolytic therapy stage. This paves the way for the development of diagnostic tools to help obstetricians make better decisions on treatments, hospital stays and admitting TPL women, and can therefore reduce costs and improve maternal and fetal wellbeing.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 by the Generalitat Valenciana (AICO/2019/220).Mas-Cabo, J.; Prats-Boluda, G.; Garcia-Casado, J.; Alberola Rubio, J.; Monfort-Ortiz, R.; Martinez-Saez, C.; Perales, A.... (2020). Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy. 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American Journal of Obstetrics and Gynecology, 199(4), 378.e1-378.e6. doi:10.1016/j.ajog.2008.08.003Mas-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. doi:10.1016/j.bspc.2019.04.001You, J., Kim, Y., Seok, W., Lee, S., Sim, D., Park, K. S., & Park, C. (2019). Multivariate Time–Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Labor. Journal of Electrical Engineering & Technology, 14(2), 897-916. doi:10.1007/s42835-019-00118-9Schuit, E., Scheepers, H., Bloemenkamp, K., Bolte, A., Duvekot, H., … van Eyck, J. (2015). Predictive Factors for Delivery within 7 Days after Successful 48-Hour Treatment of Threatened Preterm Labor. American Journal of Perinatology Reports, 05(02), e141-e149. doi:10.1055/s-0035-1552930Liao, J. B., Buhimschi, C. S., & Norwitz, E. R. (2005). Normal Labor: Mechanism and Duration. Obstetrics and Gynecology Clinics of North America, 32(2), 145-164. doi:10.1016/j.ogc.2005.01.001Ye-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. Computational and Mathematical Methods in Medicine, 2014, 1-11. doi:10.1155/2014/47078

Dispersion Entropy: A Measure of Electrohysterographic Complexity for Preterm Labor Discrimination

[EN] Although preterm labor is a major cause of neonatal death and often leaves health sequels in the survivors, there are no accurate and reliable clinical tools for preterm labor prediction. The Electrohysterogram (EHG) has arisen as a promising alternative that provides relevant information on uterine activity that could be useful in predicting preterm labor. In this work, we optimized and assessed the performance of the Dispersion Entropy (DispEn) metric and compared it to conventional Sample Entropy (SampEn) in EHG recordings to discriminate term from preterm deliveries. For this, we used the two public databases TPEHG and TPEHGT DS of EHG recordings collected from women during regular checkups. The 10th, 50th and 90th percentiles of entropy metrics were computed on whole (WBW) and fast wave high (FWH) EHG bandwidths, sweeping the DispEn and SampEn internal parameters to optimize term/preterm discrimination. The results revealed that for both the FWH and WBW bandwidths the best separability was reached when computing the 10th percentile, achieving a p-value (0.00007) for DispEn in FWH, c = 7 and m = 2, associated with lower complexity preterm deliveries, indicating that DispEn is a promising parameter for preterm labor prediction.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).Nieto-Del-Amor, F.; Ye Lin, Y.; Garcia-Casado, J.; Díaz-Martínez, MDA.; González Martínez, M.; Monfort-Ortiz, R.; Prats-Boluda, G. (2021). Dispersion Entropy: A Measure of Electrohysterographic Complexity for Preterm Labor Discrimination. SCITEPRESS. 260-267. https://doi.org/10.5220/0010316602600267S26026

Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors

[EN] Electrohysterogram (EHG) is a promising method for noninvasive monitoring of uterine electrical activity. The main purpose of this study was to characterize the multichannel EHG signals to distinguish between term delivery and preterm birth, as well as deliveries within and beyond 24 h. A total of 219 pregnant women were grouped in two ways: (1) term delivery (TD), threatened preterm labor (TPL) with the outcome of preterm birth (TPL_PB), and TPL with the outcome of term delivery (TPL_TD); (2) EHG recording time to delivery (TTD) 24 h. Three bipolar EHG signals were analyzed for the 30 min recording. Six EHG features between multiple channels, including multivariate sample entropy, mutual information, correlation coefficient, coherence, direct partial Granger causality, and direct transfer entropy, were extracted to characterize the coupling and information flow between channels. Significant differences were found for these six features between TPL and TD, and between TTD 24 h. No significant difference was found between TPL_PB and TPL_TD. The results indicated that EHG signals of TD were more regular and synchronized than TPL, and stronger coupling between multichannel EHG signals was exhibited as delivery approaches. In addition, EHG signals propagate downward for the majority of pregnant women regardless of different labors. In conclusion, the coupling and propagation features extracted from multichannel EHG signals could be used to differentiate term delivery and preterm birth and may predict delivery within and beyond 24 h.This research was funded by the National Key R&D Program, grant number 2019YFC0119700, and the National Natural Science Foundation of China, grant number U20A20388.Zhang, Y.; Hao, D.; Yang, L.; Zhou, X.; Ye Lin, Y.; Yang, Y. (2022). Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors. Sensors. 22(9):1-18. https://doi.org/10.3390/s2209335211822

Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography

[EN] Electrohysterography (EHG) has emerged as an alternative technique to predict preterm labor, which still remains a challenge for the scientific-technical community. Based on EHG parameters, complex classification algorithms involving non-linear transformation of the input features, which clinicians found difficult to interpret, were generally used to predict preterm labor. We proposed to use genetic algorithm to identify the optimum feature subset to predict preterm labor using simple classification algorithms. A total of 203 parameters from 326 multichannel EHG recordings and obstetric data were used as input features. We designed and validated 3 base classifiers based on k-nearest neighbors, linear discriminant analysis and logistic regression, achieving F1-score of 84.63 ± 2.76%, 89.34 ± 3.5% and 86.87 ± 4.53%, respectively, for incoming new data. The results reveal that temporal, spectral and non-linear EHG parameters computed in different bandwidths from multichannel recordings provide complementary information on preterm labor prediction. We also developed an ensemble classifier that not only outperformed base classifiers but also reduced their variability, achieving an F1-score of 92.04 ± 2.97%, which is comparable with those obtained using complex classifiers. Our results suggest the feasibility of developing a preterm labor prediction system with high generalization capacity using simple easy-to-interpret classification algorithms to assist in transferring the EHG technique to clinical practice.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 by the Generalitat Valenciana (AICO/2019/220).Nieto-Del-Amor, F.; Prats-Boluda, G.; Martínez-De-Juan, JL.; Díaz-Martínez, MDA.; Monfort-Ortiz, R.; Diago-Almela, VJ.; Ye Lin, Y. (2021). Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography. Sensors. 21(10):1-15. https://doi.org/10.3390/s21103350S115211

Classification Techniques Using EHG Signals for Detecting Preterm Births

Premature birth is defined as an infant born before 37 weeks of gestation and can be sub-categorized into three phrases; late preterm delivery between 34 and 36 weeks of gestation; moderately preterm between 32 and 34 weeks, and extreme preterm less than 28 weeks of gestation. Globally, the rate of preterm births is increasing, thus resulting in significant health, development and economic problems. The current methods for the detection of preterm birth are inadequate due to the fact that the exact cause of premature uterine contractions leading to delivery is mostly unknown. Another problem is the interpretation of temporal and spectral characteristics of Electromyography (EMG), which is an electrodiagnostic medicine technique for recording and evaluating the electrical activity produced by uterine muscles during pregnancy and parturition – significant variability exists among obstetric care practitioners. Apart from a small number of potential causes for preterm birth, such as medication, uterine over-distension, preterm premature rupture of membranes (PPROM), intrauterine inflammation, precocious foetal endocrine activation, surgery, ethnicity and lifestyle, there is still a large amount of uncertainty about their specific risks. Hence, it is currently very difficult to make reliable predictions about preterm delivery risk. There has also been some evidence that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect the onset of preterm delivery. Early detection opens up new avenues for the development of an automated ambulatory system, based on uterine EMG, for patient monitoring during pregnancy. This can be made possible through the use of machine learning. The essence of machine learning is the utilisation of previously recorded data outcomes to train algorithms to ii stimulate software learning elements. Such learned models can, as a result, be used to detect and predict the early signs associated with the onset of preterm birth. Therefore in this thesis, Electrohysterography signals are used to classify uterine activity associated with preterm birth. This is achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies are utilized, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. The results illustrate that the combination of the Levenberg-Marquardt trained Feed-Forward Neural Network, Radial Basis Function Neural Network and the Random Neural Network classifiers performed the best, with 91% for sensitivity, 84% for specificity, 94% for the area under the curve and 12% for the mean error rate. Applying advanced machine learning algorithms, in conjunction with innovative signal processing techniques and the analysis of Electrohysterography signals shows significant benefits for use in clinical interventions for preterm birth assessments

Desarrollo de un sistema de predicción del parto prematuro basado en electrohisterografía y algoritmo genético para la selección de características óptimas

[ES] El parto prematuro es una de las complicaciones más comunes y la primera causa de muerte en niños menores de 5 años, una de las causas que mayores complicaciones de salud provocará a lo largo de la vida del sujeto, así como un gran coste económico para la sociedad. Los mecanismos que inducen este suceso todavía no están claros. Debido a que las contracciones uterinas son dadas por la actividad eléctrica del miometrio, su medición de manera externa, pueden ayudar a entender los mecanismos de inducción al parto y el origen del parto prematuro. Para ello, se introduce la técnica conocida como electromiograma uterino (EMG) o electrohisterograma (EHG), la cual se basa en el registro no invasivo de la actividad eléctrica relacionada con la contracción de las células miometriales del útero. Son diversos los estudios que han demostrado que mediante las características obtenidas por EHG es posible discernir entre contracciones “verdaderas” o “falsas” en los embarazos a término y prematuros. El objetivo del presente estudio es desarrollar un sistema de predicción del parto prematuro en mujeres de control rutinario en base a las características de EHG utilizando algoritmos computacionalmente eficientes. Para ello, se han comparado distintas técnicas de reducción de dimensionalidad para determinar las características relevantes: Análisis de Componentes Principales (PCA) y Algoritmo Genético (AG). Además, se han implementado y comparado los siguientes métodos de clasificación: Análisis Discriminante Lineal (LDA), Regresión Logística (RL), K vecinos más próximos (KNN) y Extreme Learning Machine (ELM). Los resultados muestran que el algoritmo genético permite obtener la combinación óptima de características con información complementaria para LDA y RL, obteniendo un F1-Score promedio de 89,03% y 85,78% en el grupo de test respectivamente. En cambio, PCA ha obtenido mejores resultados que el algoritmo genético cuando se emplea KNN como método de clasificación, obteniendo un F1-Score promedio de 90,19%. En base a estos clasificadores individuales, se ha implementado un clasificador combinado con la técnica de votación por mayoría, obteniendo un F1-Score promedio del 93% en el grupo de test. Estos resultados sugieren la posibilidad de desarrollar sistemas de predicción del parto prematuro precisos y generalizables en mujeres de control rutinario en base a las características de EHG. Además, estos sistemas podrían tener una gran aceptabilidad por el personal clínico al ser métodos simples y de uso clínico, mejorando así la transferibilidad de la técnica de EHG a la praxis clínica.[CA] El part prematur és una de les complicacions més habituals i la principal causa de mort en xiquets menors de cinc anys, un dels motius que permetran complicacions importants per a la salut al llarg de la vida del subjecte, així com un gran cost econòmic per a la societat. Els mecanismes que indueixen aquest esdeveniment encara no estan clars. Com que l’activitat elèctrica del miometri dona contracció uterina, la seua mesura externa pot ajudar a comprendre els mecanismes d’inducció del treball i l’origen del treball prematur. Per a això, s’introdueix la tècnica que es coneix com a electromiograma uterí (EMG), o l’electrohisterograma (EHG), que es basa en el registre no invasiu d’activitat elèctrica relacionada amb la contracció de cèl·lules miometrials a l’úter. Diversos estudis han demostrat que utilitzant les característiques obtingudes per EHG és possible distingir entre contraccions "verdaderes" o "falses" en embarassos prematurs i a terme. L’objectiu del present estudi és desenvolupar un sistema de predicció del part prematur en dones sota control rutinari basat en les característiques d'EHG mitjançant algoritmes eficients computacionalment. Per a això, s'han diferenciat tècniques de reducció de la dimensionalitat per determinar les característiques rellevants: Anàlisi de Components Principals (PCA) i Algoritme Genètic (AG). A més, s'han implementat i comparats els següents mètodes de classificació: Anàlisi Discriminant Lineal (LDA), Regressió Logística (RL), K veïns més pròxims (KNN) i Extreme Learning Machine (ELM). Els resultats mostren que l’algoritme genètic permet obtindre l’òptima combinació de característiques amb informació complementària per a LDA i RL, i s'ha aconseguit una puntuació F1 del 89,03% i el 85,78% en el grup de prova, respectivament. En canvi, PCA ha obtingut millors resultats que l'algoritme genètic quan s'utilitza KNN com a mètode de classificació, ja que ha obtingut una puntuació F1 del 90,19%. Sobre la base d'aquests classificadors individuals, un classificador combinat amb la tècnica de la votació per majoria ha sigut executat, i ha aconseguit una F1-Score de 93% en el grup de prova. Aquests resultats suggereixen la possibilitat de desenvolupar un sistema exacte i generalitzable de predicció del treball prematur en dones controlades de forma rutinària basat en les característiques d'EHG. A més, aquests sistemes podrien tindre una excel·lent acceptabilitat per part del personal clínic, ja que són mètodes senzills i d'ús clínic, que milloraran així la transferibilitat de la tècnica EHG a la praxi clínica.[EN] Preterm labour is one of the most common complications and the leading cause of death in children under five years old, one of the reasons that will let significant health complications throughout the life of the subject, as well as a tremendous economic cost for the society. The mechanisms that induce this event are still unclear because the electrical activity of the myometrium gives uterine contraction; its measurement externally can help to understand the mechanisms of labour induction and the origin of premature labour. For this, the technique is known as uterine electromyogram (EMG), or electrohysterogram (EHG) is introduced, which is based on the non-invasive recording of electrical activity related to the contraction of myometrial cells in the uterus. Several studies have shown that using the characteristics obtained by EHG. It is possible to discern between "true" or "false" contractions in term and premature pregnancies. The objective of the present study is to develop a system for predicting preterm birth in women under routine control based on the characteristics of EHG using computationally efficient algorithms. For this, different dimensionality reduction techniques have been differentiated to determine the relevant characteristics: Principal Component Analysis (PCA) and Genetic Algorithm (GA). Besides, the following classification methods have been implemented and compared: Linear Discriminant Analysis (LDA), Logistic Regression (RL), K-Nearest Neighbors (KNN), Extreme Learning Machine (ELM). The results show that the genetic algorithm allows obtaining the optimal combination of characteristics with complementary information for LDA and RL, reaching an F1-Score of 89.03% and 85.78% in the test group, respectively. In contrast, PCA has achieved better results than the genetic algorithm when KNN is used as the classification method, obtaining an F1-Score of 90.19%. Based on these individual classifiers, a classifier combined with the majority voting technique has been executed, getting an F1-Score of 93% in the test group. These results suggest the possibility of developing an accurate and generalizable system of prediction of premature labour in routinely controlled women based on the characteristics of EHG. Furthermore, these systems could have excellent acceptability by clinical personnel as they are simple methods. As well, and for clinical use, thus improving the transferability of the EHG technique to clinical praxis.García Borillo, D. (2020). Desarrollo de un sistema de predicción del parto prematuro basado en electrohisterografía y algoritmo genético para la selección de características óptimas. http://hdl.handle.net/10251/147935TFG

Estudio comparativo de las diferentes medidas de entropía para la predicción del parto prematuro

[ES] El parto prematuro es una situación de alto riesgo que tiene una prevalencia superior al 10% de los partos, afectando a más de 15 millones de familias en el mundo. Sus repercusiones se muestran tanto en la salud materno fetal, siendo la principal causa de muerte en niños menores de 5 años como en el sobrecoste económico que supone a los sistemas sanitarios de los países. En este trabajo se ha llevado a cabo un estudio comparativo de diferentes medidas de entropía obtenidas de registro no invasivos de la actividad mioeléctrica uterina, electrohisterograma (EHG) en mujeres gestantes que acuden a controles rutinarios del embarazo, para discernir entre el parto a término y prematuro. Con dicho fin han sido analizadas dos bases de datos públicas de registros EHG de mujeres que dieron a luz a término y pretérmino, computándose las siguientes medidas de entropía: entropía muestral, entropía muestral multivariable, entropía difusa, entropía difusa multivariable, entropía de dispersión, entropía de dispersión multivariable, entropía de burbuja y entropía de transferencia. Para cada para cada una de estas medidas se ha realizado un barrido de sus parámetros internos seleccionándose la combinación óptima de los mismos en función de su capacidad para separar entre los dos grupos a discriminar (parto prematuro vs término) de acuerdo con las pruebas estadísticas de Wilconxon y de Kolmogórov-Smirnov. Tras obtener la combinación óptima de parámetros para las diferentes métricas de entropía, se ha valorado el desempeño de un clasificador kNN que emplea estas métricas y otros parámetros temporales y espectrales de las señales de EHG, con el que se ha llegado a obtener un F1 score de 92,23% ± 2,09%.[CA] El part prematur és una situació d'alt risc que té una prevalença superior al 10% dels parts, afectant més de 15 milions de famílies en el món. Les seues repercussions es mostren tant en la salut matern fetal, sent la principal causa de mort en xiquets menors de 5 anys com en el sobrecost econòmic que suposa als sistemes sanitaris dels països. En este treball s'ha dut a terme un estudi comparatiu de diferents mesures d'entropia obtingudes de registre no invasius de l'activitat mioeléctrica uterina, electrohisterograma (EHG) en dones gestants que acudixen a controls rutinaris de l'embaràs, per a discernir entre el part a terme i prematur. Amb el dit fi han sigut analitzades dos bases de dades públiques de registres EHG de dones que van donar a llum a terme i preterme, computant-se les següents mesures d'entropia: entropia mostral, entropia mostral multivariable, entropia difusa, entropia difusa multivariable, entropia de dispersió, entropia de dispersió multivariable, entropia de bambolla i entropia de transferència. Per a cada per a cada una d'estes mesures s'ha realitzat un agranat dels seus paràmetres interns seleccionant-se la combinació òptima dels mateixos en funció de la seua capacitat per a separar entre els dos grups a discriminar (part prematur vs terme) d'acord amb les proves estadístiques de Wilconxon i de Kolmogórov-Smirnov. Després d'obtindre la combinació òptima de paràmetres per a les diferents mètriques d'entropia, s'ha valorat l'exercici d'un classificador kNN que empra estes mètriques i altres paràmetres temporals i espectrals dels senyals d'EHG, amb el que s'ha arribat a obtindre un F1 score de 92,23% ± 2,09%.[EN] The preterm labor is a high-risk situation which has a prevalence up to 10% of all labors, affecting to more than 15 million families worldwide. The consequences are shown both in affected maternal-fetal health, being the main mortality cause in children under 5 years old, and in the economic costs which suppose to the healthcare systems of the countries. In this paper is performed a comparative research among different entropy metrics obtained from non-invasive registers of myoelectric uterine activity, electrohysterography (EHG) in pregnant women who goes to ordinary labor controls, aiming to preterm labor prediction. With this target has been analysed two public EHG register data bases of women who delivered term and preterm, computing the following non-linear metrics: sample entropy, multivariate sample entropy, fuzzy entropy, multivariate fuzzy entropy, dispersion entropy, multivariate dispersion entropy, bubble entropy and transfer entropy. With each of these metrics has been perform a sweep of their internal parameters, selecting the optimal combination regarding their capacity of separate among term and preterm groups, according to the Wilconxon Rank-Sum Test and Kolmogorov-Smirnov distance. When the optimal parameter combination has been gotten, for the different entropy metrics, the performance of an kNN classifier has been assessed using these and other temporal and spectral metrics of EHG signals, getting a F1 score of 92.23% ± 2.09%.Nieto Del Amor, F. (2020). Estudio comparativo de las diferentes medidas de entropía para la predicción del parto prematuro. Universitat Politècnica de València. http://hdl.handle.net/10251/161660TFG

A Comparative Study of Vaginal Labor and Caesarean Section Postpartum Uterine Myoelectrical Activity

[EN] Postpartum hemorrhage (PPH) is one of the major causes of maternal mortality and morbidity worldwide, with uterine atony being the most common origin. Currently there are no obstetrical techniques available for monitoring postpartum uterine dynamics, as tocodynamometry is not able to detect weak uterine contractions. In this study, we explored the feasibility of monitoring postpartum uterine activity by non-invasive electrohysterography (EHG), which has been proven to outperform tocodynamometry in detecting uterine contractions during pregnancy. A comparison was made of the temporal, spectral, and non-linear parameters of postpartum EHG characteristics of vaginal deliveries and elective cesareans. In the vaginal delivery group, EHG obtained a significantly higher amplitude and lower kurtosis of the Hilbert envelope, and spectral content was shifted toward higher frequencies than in the cesarean group. In the non-linear parameters, higher values were found for the fractal dimension and lower values for Lempel-Ziv, sample entropy and spectral entropy in vaginal deliveries suggesting that the postpartum EHG signal is extremely non-linear but more regular and predictable than in a cesarean. The results obtained indicate that postpartum EHG recording could be a helpful tool for earlier detection of uterine atony and contribute to better management of prophylactic uterotonic treatment for PPH prevention.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 (GV/2018/104 and AICO/2019/220).Díaz-Martínez, MDA.; Mas-Cabo, J.; Prats-Boluda, G.; Garcia-Casado, J.; Cardona-Urrego, K.; Monfort-Ortiz, R.; Lopez-Corral, A.... (2020). A Comparative Study of Vaginal Labor and Caesarean Section Postpartum Uterine Myoelectrical Activity. 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