Design and assessment of a computer-assisted artificial intelligence system for predicting preterm labor in women attending regular check-ups. Emphasis in imbalance data learning technique

Tesis por compendio[ES] El parto prematuro, definido como el nacimiento antes de las 37 semanas de gestación, es una importante preocupación mundial con implicaciones para la salud de los recién nacidos y los costes económicos. Afecta aproximadamente al 11% de todos los nacimientos, lo que supone más de 15 millones de individuos en todo el mundo. Los métodos actuales para predecir el parto prematuro carecen de precisión, lo que conduce a un sobrediagnóstico y a una viabilidad limitada en entornos clínicos. La electrohisterografía (EHG) ha surgido como una alternativa prometedora al proporcionar información relevante sobre la electrofisiología uterina. Sin embargo, los sistemas de predicción anteriores basados en EHG no se han trasladado de forma efectiva a la práctica clínica, debido principalmente a los sesgos en el manejo de datos desbalanceados y a la necesidad de modelos de predicción robustos y generalizables. Esta tesis doctoral pretende desarrollar un sistema de predicción del parto prematuro basado en inteligencia artificial utilizando EHG y datos obstétricos de mujeres sometidas a controles prenatales regulares. Este sistema implica la extracción de características relevantes, la optimización del subespacio de características y la evaluación de estrategias para abordar el reto de los datos desbalanceados para una predicción robusta. El estudio valida la eficacia de las características temporales, espectrales y no lineales para distinguir entre casos de parto prematuro y a término. Las nuevas medidas de entropía, en concreto la dispersión y la entropía de burbuja, superan a las métricas de entropía tradicionales en la identificación del parto prematuro. Además, el estudio trata de maximizar la información complementaria al tiempo que minimiza la redundancia y las características de ruido para optimizar el subespacio de características para una predicción precisa del parto prematuro mediante un algoritmo genético. Además, se ha confirmado la fuga de información entre el conjunto de datos de entrenamiento y el de prueba al generar muestras sintéticas antes de la partición de datos, lo que da lugar a una capacidad de generalización sobreestimada del sistema predictor. Estos resultados subrayan la importancia de particionar y después remuestrear para garantizar la independencia de los datos entre las muestras de entrenamiento y de prueba. Se propone combinar el algoritmo genético y el remuestreo en la misma iteración para hacer frente al desequilibrio en el aprendizaje de los datos mediante el enfoque de particio'n-remuestreo, logrando un área bajo la curva ROC del 94% y una precisión media del 84%. Además, el modelo demuestra un F1-score y una sensibilidad de aproximadamente el 80%, superando a los estudios existentes que consideran el enfoque de remuestreo después de particionar. Esto revela el potencial de un sistema de predicción de parto prematuro basado en EHG, permitiendo estrategias orientadas al paciente para mejorar la prevención del parto prematuro, el bienestar materno-fetal y la gestión óptima de los recursos hospitalarios. En general, esta tesis doctoral proporciona a los clínicos herramientas valiosas para la toma de decisiones en escenarios de riesgo materno-fetal de parto prematuro. Permite a los clínicos diseñar estrategias orientadas al paciente para mejorar la prevención y el manejo del parto prematuro. La metodología propuesta es prometedora para el desarrollo de un sistema integrado de predicción del parto prematuro que pueda mejorar la planificación del embarazo, optimizar la asignación de recursos y reducir el riesgo de parto prematuro.[CA] El part prematur, definit com el naixement abans de les 37 setmanes de gestacio', e's una important preocupacio' mundial amb implicacions per a la salut dels nounats i els costos econo¿mics. Afecta aproximadament a l'11% de tots els naixements, la qual cosa suposa me's de 15 milions d'individus a tot el mo'n. Els me¿todes actuals per a predir el part prematur manquen de precisio', la qual cosa condueix a un sobrediagno¿stic i a una viabilitat limitada en entorns cl¿'nics. La electrohisterografia (EHG) ha sorgit com una alternativa prometedora en proporcionar informacio' rellevant sobre l'electrofisiologia uterina. No obstant aixo¿, els sistemes de prediccio' anteriors basats en EHG no s'han traslladat de manera efectiva a la pra¿ctica cl¿'nica, degut principalment als biaixos en el maneig de dades desequilibrades i a la necessitat de models de prediccio' robustos i generalitzables. Aquesta tesi doctoral prete'n desenvolupar un sistema de prediccio' del part prematur basat en intel·lige¿ncia artificial utilitzant EHG i dades obste¿triques de dones sotmeses a controls prenatals regulars. Aquest sistema implica l'extraccio' de caracter¿'stiques rellevants, l'optimitzacio' del subespai de caracter¿'stiques i l'avaluacio' d'estrate¿gies per a abordar el repte de les dades desequilibrades per a una prediccio' robusta. L'estudi valguda l'efica¿cia de les caracter¿'stiques temporals, espectrals i no lineals per a distingir entre casos de part prematur i a terme. Les noves mesures d'entropia, en concret la dispersio' i l'entropia de bambolla, superen a les me¿triques d'entropia tradicionals en la identificacio' del part prematur. A me's, l'estudi tracta de maximitzar la informacio' complementa¿ria al mateix temps que minimitza la redunda¿ncia i les caracter¿'stiques de soroll per a optimitzar el subespai de caracter¿'stiques per a una prediccio' precisa del part prematur mitjan¿cant un algorisme gene¿tic. A me's, hem confirmat la fugida d'informacio' entre el conjunt de dades d'entrenament i el de prova en generar mostres sinte¿tiques abans de la particio' de dades, la qual cosa dona lloc a una capacitat de generalitzacio' sobreestimada del sistema predictor. Aquests resultats subratllen la importa¿ncia de particionar i despre's remostrejar per a garantir la independe¿ncia de les dades entre les mostres d'entrenament i de prova. Proposem combinar l'algorisme gene¿tic i el remostreig en la mateixa iteracio' per a fer front al desequilibri en l'aprenentatge de les dades mitjan¿cant l'enfocament de particio'-remostrege, aconseguint una a¿rea sota la corba ROC del 94% i una precisio' mitjana del 84%. A me's, el model demostra una puntuacio' F1 i una sensibilitat d'aproximadament el 80%, superant als estudis existents que consideren l'enfocament de remostreig despre's de particionar. Aixo¿ revela el potencial d'un sistema de prediccio' de part prematur basat en EHG, permetent estrate¿gies orientades al pacient per a millorar la prevencio' del part prematur, el benestar matern-fetal i la gestio' o¿ptima dels recursos hospitalaris. En general, aquesta tesi doctoral proporciona als cl¿'nics eines valuoses per a la presa de decisions en escenaris de risc matern-fetal de part prematur. Permet als cl¿'nics dissenyar estrate¿gies orientades al pacient per a millorar la prevencio' i el maneig del part prematur. La metodologia proposada e's prometedora per al desenvolupament d'un sistema integrat de prediccio' del part prematur que puga millorar la planificacio' de l'embara¿s, optimitzar l'assignacio' de recursos i millorar la qualitat de l'atencio'.[EN] Preterm delivery, defined as birth before 37 weeks of gestation, is a significant global concern with implications for the health of newborns and economic costs. It affects approximately 11% of all births, amounting to more than 15 million individuals worldwide. Current methods for predicting preterm labor lack precision, leading to overdiagnosis and limited practicality in clinical settings. Electrohysterography (EHG) has emerged as a promising alternative by providing relevant information about uterine electrophysiology. However, previous prediction systems based on EHG have not effectively translated into clinical practice, primarily due to biases in handling imbalanced data and the need for robust and generalizable prediction models. This doctoral thesis aims to develop an artificial intelligence based preterm labor prediction system using EHG and obstetric data from women undergoing regular prenatal check-ups. This system entails extracting relevant features, optimizing the feature subspace, and evaluating strategies to address the imbalanced data challenge for robust prediction. The study validates the effectiveness of temporal, spectral, and non-linear features in distinguishing between preterm and term labor cases. Novel entropy measures, namely dispersion and bubble entropy, outperform traditional entropy metrics in identifying preterm labor. Additionally, the study seeks to maximize complementary information while minimizing redundancy and noise features to optimize the feature subspace for accurate preterm delivery prediction by a genetic algorithm. Furthermore, we have confirmed leakage information between train and test data set when generating synthetic samples before data partitioning giving rise to an overestimated generalization capability of the predictor system. These results emphasize the importance of using partitioning-resampling techniques for ensuring data independence between train and test samples. We propose to combine genetic algorithm and resampling method at the same iteration to deal with imbalanced data learning using partition-resampling pipeline, achieving an Area Under the ROC Curve of 94% and Average Precision of 84%. Moreover, the model demonstrates an F1-score and recall of approximately 80%, outperforming existing studies on partition-resampling pipeline. This finding reveals the potential of an EHG-based preterm birth prediction system, enabling patient-oriented strategies for enhanced preterm labor prevention, maternal-fetal well-being, and optimal hospital resource management. Overall, this doctoral thesis provides clinicians with valuable tools for decision-making in preterm labor maternal-fetal risk scenarios. It enables clinicians to design a patient-oriented strategies for enhanced preterm birth prevention and management. The proposed methodology holds promise for the development of an integrated preterm birth prediction system that can enhance pregnancy planning, optimize resource allocation, and ultimately improve the outcomes for both mother and baby.Nieto Del Amor, F. (2023). Design and assessment of a computer-assisted artificial intelligence system for predicting preterm labor in women attending regular check-ups. Emphasis in imbalance data learning technique [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/200900Compendi

Diseño de una cámara Térmica IR de bajo coste basada en microcontrolador

[ES] En el presente Proyecto Final de Carrera se ha desarrollado la implementación, integración y soporte para una cámara térmica, que puede ser de utilidad en áreas donde se requiera la inspección térmica de superficies. Para su desarrollo, en primer lugar, se ha desarrollado el código de programación para conseguir comunicar un sensor IR, con una pantalla LCD a través de una tarjeta de programación Arduino Mega 2650 rev3. La finalidad ha sido lograr proporcionar al usuario de la misma una interfaz interactiva, así como el desarrollo de aplicaciones que puedan ser útiles durante el uso de la misma. Así, se ha desarrollado una barra térmica, un analizador de imagen y la capacidad de almacenamiento de datos en una tarjeta microSD. En segundo lugar, se ha implementado un sistema de alimentación autónomo con la finalidad de dar soporte energético integrado a la cámara térmica. Para su realización se ha hecho uso de una batería de Ion-Litio, un convertidor Boost y un sistema BMS. Los objetivos han sido dotar de autonomía al proyecto mediante un sistema recargable de energía. En tercer lugar, se ha desarrollado el modelado de la carcasa que servirá de soporte a todos los componentes, logrando formar un único sistema mediante el cual el usuario no tenga interacción con los componentes que la conforman, a la vez de dar protección al conjunto. Para su modelado físico se ha realizado su impresión 3D. Finalmente, notar que pretende ser una alternativa económica ante las diferentes ofertas que oferta el mercado.[EN] In this Final Career Project has been developed the implementation, integration and support for a thermal camera. This camera can use for studying thermal surfaces. Firstly, programming code has been developed aiming to communicate the thermal camera with LCD screen, through Arduino Mega 2650 rev3 programming board. The purpose of this is to get an interactive interface and to create applications which may be useful for the camera management. For that, has been created a thermal bar, a picture analyser and skill to save information in a microSD card. Secondly, it has been implemented an autonomous feed system to give energetic support to the camera. It needed an ion-lithium battery, a Boost converter and a BMS system. In fact, the target of this goal has been given autonomy to the project thought a recharging energy system. Thirdly, a model of case has been created which can use to support all components, getting an only and isolated system. The thermal camera has been made with 3D system. Finally, this thermic camera can be an economy alternative among different offers which It¿s in the market.Nieto Del Amor, F. (2018). Diseño de una cámara Térmica IR de bajo coste basada en microcontrolador. http://hdl.handle.net/10251/107689TFG

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

Inception 1D-convolutional neural network for accurate prediction of electrical insulator leakage current from environmental data during its normal operation using long-term recording

[EN] Contamination flashover remains one of the biggest challenges for power grid designers and maintenance engineers. Insulator leakage current contains relevant information about their state so that continuous monitoring is considered the most effective way to prevent contamination flashover. In this work, we attempted to accurately predict insulator leakage current in real time during normal operations based on environmental data using long-term recordings. We first confirmed that the history of environmental data also contained relevant information to predict leakage current by conditional Granger analysis and determined that 20 was the optimal previous samples number for this purpose. We then compared the performance of typical regression models and convolutional neural network (CNN), when using both current and the last 21 samples as input features. We confirmed that the model with the last 21 samples might perform significantly better. Input features pre-processing by cascaded inception architecture was fundamental to capture the complex dynamic interaction between environmental data and leakage current and significantly improved the model performance. CNN based on inception architecture performed much better, achieving an average R2 of 0.94 ±0.03. The proposed model could be used to predict leakage current in both porcelain insulators with or without coatings and silicone composite insulators. Our results pave the way for creating an on-line pre-warning system adapted to individual installations, can anticipate the negative consequences of weather and/or pollution deposits and is useful for designing a strategic high-voltage electrical insulator preventive maintenance plan for preventing contamination flashover and thus increase power grid reliability and resilience.This work was supported by the Spanish Ministry of Economy and Competitiveness, Spain, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR) .Bueno Barrachina, JM.; Ye Lin, Y.; Nieto Del-Amor, F.; Fuster Roig, VL. (2023). Inception 1D-convolutional neural network for accurate prediction of electrical insulator leakage current from environmental data during its normal operation using long-term recording. Engineering Applications of Artificial Intelligence. 119. https://doi.org/10.1016/j.engappai.2022.10579911

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

Uterine myoelectrical activity as biomarker of successful induction with Dinoprostone: Influence of parity

[EN] The prolonged latent phase of Induction of Labour (IOL) is associated with increased risks of maternal mortality and morbidity. Electrohysterography (EHG) has outperformed traditional clinical measures monitoring labour progress. Although parity is agreed to be of particular relevance to the success of IOL, no previous EHG¿related studies have been found in the literature. We thus aimed to identify EHG¿biomarkers to predict IOL success (active phase of labour in¿¿¿24¿h) and determine the influence of the myoelectrical response on the parity of this group. Statistically significant and sustained differences between the successful and failed groups were found from 150¿min in amplitude and non¿linear parameters, especially in Spectral Entropy and in their progression rates. In the nulliparous¿parous comparison, parous women showed statistically significantly higher amplitude progression rate. These biomarkers would therefore be useful for early detection of the risk of induction failure and would help to develop more robust and generalizable IOL success¿prediction systems.This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR and PID2021-124038OB-I00). Funding for open access charge: CRUE-Universitat Politècnica de ValènciaDiaz-Martinez, A.; Monfort-Ortiz, R.; Ye Lin, Y.; Garcia-Casado, J.; Nieto-Tous, M.; Nieto Del-Amor, F.; Diago-Almela, VJ.... (2023). Uterine myoelectrical activity as biomarker of successful induction with Dinoprostone: Influence of parity. Biocybernetics and Biomedical Engineering (Online). 43(1):142-156. https://doi.org/10.1016/j.bbe.2022.12.00414215643

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

Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals

[EN] One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of 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.; Beskhani, R.; Ye Lin, Y.; Garcia-Casado, J.; Díaz-Martínez, MDA.; Monfort-Ortiz, R.; Diago-Almela, VJ.... (2021). Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals. Sensors. 21(18):1-17. https://doi.org/10.3390/s21186071S117211

Recurrence quantification analysis of uterine vectormyometriogram to identify pregnant women with threatened preterm labor

[EN] Electrohysterography has been shown to provide relevant information on preventing preterm labor. Recent studies have confirmed the feasibility of using the vectormyometriogram (VMG) to assess uterine myoelectric vector displacement, with different physiological implications for the slow and fast waves, without suggesting its implementation in clinical practice. The fast wave VMG component has dynamic behavior in any specific direction on the X-Y plane. Since recurrence is a common feature of dynamic systems, we aimed to determine the recurrence pattern of uterine vector displacement, exploring its clinical potential in detecting imminent and preterm labor in women with threatened preterm labor and a serious preterm birth risk. For this, we analyzed the recurrence patterns from a 2D-vectormyometriogram using four common statistics: determinism, longest diagonal, entropy, and laminarity. We found significantly increased determinism (0.035 ± 0.011 vs. 0.077 ± 0.041), entropy (1.768 ± 0.116 vs. 2.197 ± 0.24) and laminarity (0.086 ± 0.034 vs. 0.173 ± 0.078) from the early (26¿30 weeks) to late (35¿37 weeks) gestation stages. As pregnancy progresses, the uterine vector displacement becomes more periodic, predictable and stable, while VMG recurrence statistics in the fast wave high bandwidth better detect imminent and preterm labor, outperforming classical EHG parameters from bipolar channels. The proposed method was also resistant to motion artifacts and preserved its discriminative capacity between the groups. Our results on VMG recurrence statistics could thus be another reliable biomarker for preventing preterm labor in women with threatened preterm labor and would favor transferring the EHG technique to clinical practice.This work was supported by the Spanish Ministry of Science and Innovation and the European Regional Development Fund, State Plan for Scientific, Technical and Innovation Research 2021 - 2023 (PID2021-124038OB-I00) . 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.Nieto Del-Amor, F.; Prats-Boluda, G.; Li, W.; Martínez-De-Juan, JL.; Yang, L.; Yang, Y.; Hao, D.... (2024). Recurrence quantification analysis of uterine vectormyometriogram to identify pregnant women with threatened preterm labor. Biomedical Signal Processing and Control. 89. https://doi.org/10.1016/j.bspc.2023.1057958

Comparative Study of Uterine Myoelectrical Response to Labour Induction Drugs in Nulliparous and Parous Women with Different EHG Analysis Techniques

[EN] Induction of labour (IOL) is one of the most widespread practices to promote the onset of labour when maternal-fetal well-being is compromised. Currently, the monitoring of this procedure in clinical practice is performed with subjective and poorly reproducible techniques such as tocography and cervical assessment, without taking into account other obstetric variables of special relevance as parity. Electrohysterography (EHG) has emerged as a promising alternative due to its usefulness and non-invasiveness. Traditionally, EHG has been characterized by analyzing the EHG-bursts (EBA) associated with uterine contractions and computing temporal, spectral and nonlinear parameters. Recent studies characterize the EHG by considering both EHG-burst and basal activity (WEWA). The first objective of this study was to discern which analysis technique presented the best performance for EHG characterization during IOL. Subsequently, differences in uterine myoelectric response to IOL drugs in nulliparous and parous women were analyzed and compared. EHG recordings were performed during the first 4 hours of IOL in 15 nulliparous and 10 parous women. EBA results showed a greater number of parameters with significant differences with their corresponding baseline ones than WEWA, as well as a greater slope in both parity groups. Parous women presented greater amplitude and more pronounced downward trends for nonlinear parameters than nulliparous, especially for Sample and Spectral Entropy, which is associated with a greater predisposition to achieve APL that is corroborated by obstetric variables. Moreover, future efforts seem necessary to study in depth the differences between parity groups in order to correctly characterize and interpret their evolution.The collaboration of the HUP La Fe has been fundamental for the development of this work. In addition, the Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, EU RTI2018- 094449-A-I00-AR) and the Generalitat Valenciana (AICO/2019/220) have supported it.Díaz-Martínez, MDA.; Monfort-Ortiz, R.; Ye Lin, Y.; Garcia-Casado, J.; Nieto Del-Amor, F.; Diago-Almela, VJ.; Rey-Ferreira, I.... (2021). Comparative Study of Uterine Myoelectrical Response to Labour Induction Drugs in Nulliparous and Parous Women with Different EHG Analysis Techniques. IEEE. 1-4. https://doi.org/10.1109/EHB52898.2021.9657548S1