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

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

Evidence and clinical relevance of maternal-fetal cardiac coupling:A scoping review

BACKGROUND: Researchers have long suspected a mutual interaction between maternal and fetal heart rhythms, referred to as maternal-fetal cardiac coupling (MFCC). While several studies have been published on this phenomenon, they vary in terms of methodologies, populations assessed, and definitions of coupling. Moreover, a clear discussion of the potential clinical implications is often lacking. Subsequently, we perform a scoping review to map the current state of the research in this field and, by doing so, form a foundation for future clinically oriented research on this topic.METHODS: A literature search was performed in PubMed, Embase, and Cochrane. Filters were only set for language (English, Dutch, and German literature were included) and not for year of publication. After screening for the title and the abstract, a full-text evaluation of eligibility followed. All studies on MFCC were included which described coupling between heart rate measurements in both the mother and fetus, regardless of the coupling method used, gestational age, or the maternal or fetal health condition.RESULTS: 23 studies remained after a systematic evaluation of 6,672 studies. Of these, 21 studies found at least occasional instances of MFCC. Methods used to capture MFCC are synchrograms and corresponding phase coherence indices, cross-correlation, joint symbolic dynamics, transfer entropy, bivariate phase rectified signal averaging, and deep coherence. Physiological pathways regulating MFCC are suggested to exist either via the autonomic nervous system or due to the vibroacoustic effect, though neither of these suggested pathways has been verified. The strength and direction of MFCC are found to change with gestational age and with the rate of maternal breathing, while also being further altered in fetuses with cardiac abnormalities and during labor.CONCLUSION: From the synthesis of the available literature on MFCC presented in this scoping review, it seems evident that MFCC does indeed exist and may have clinical relevance in tracking fetal well-being and development during pregnancy.</p

Etude de la propagation de l‟activité électrique utérine dans une optique clinique : Application a la détection des menaces d‟accouchement prématuré

Uterine contractions are essentially controlled by two physiological phenomena: cell excitability and propagation of uterine electrical activity probably related to high and low frequencies of uterine electromyogram, called electrohysterogram -EHG-, respectively. All previous studies have been focused on extracting parameters from the high frequency part and did not show a satisfied potential for clinical application. The objective of this thesis is the analysis of the propagation EHG signals of during pregnancy and labor in the view of extracting tool for clinical application. A novelty of our thesis is the multichannel recordings by using 4x4 electrodes matrix posed on the woman abdomen. Monovariate analysis was aimed to investigate the nonlinear characteristics of EHG signals. Bivariate and multivariate analyses have been done to analyze the propagation of the EHG signals by detecting the connectivity between the signals. An increase of the nonlinearity associated by amplitude synchronization and phase desynchronization were detected. Results indicate a highest EHG propagation during labor than pregnancy and an increase of this propagation with the week of gestations. The results show the high potential of propagation‟s parameters in clinical point of view such as labor detection and then preterm labor prediction. We proposed novel combination of Blind Source Separation and empirical mode decomposition to denoise monopolar EHG as a possible way to increase the classification rate of pregnancy and labor.Les contractions utérines sont contrôlées par deux phénomènes physiologiques: l'excitabilité cellulaire et la propagation de l'activité électrique utérine probablement liées aux hautes et basses fréquences de l‟electrohysterograme (EHG) respectivement. Toutes les études précédentes ont porté sur l'extraction de paramètres de la partie haute fréquence et n'ont pas montré un potentiel satisfait pour l'application clinique. L'objectif de cette thèse est l'analyse de propagation de l'EHG pendant la grossesse et le travail dans la vue de l'extraction des outils pour une application clinique. Une des nouveautés de la thèse est l‟enregistrement multicanaux à l'aide d‟une matrice d'électrodes 4x4 posée sur l'abdomen de la femme. Analyse monovariés visait à étudier les caractéristiques non linéaires des signaux EHG, analyses bivariées et multivariées ont été effectuées pour analyser la propagation des signaux EHG par la détection de la connectivité entre les signaux. Une augmentation de la non- linéarité associée par une synchronisation en amplitude et de désynchronisation en phase a été détectée. Les résultats indiquent plus de propagation au cours du travail que la grossesse et une augmentation de cette propagation avec les semaines de gestations. Les résultats montrent le potentiel élevé de paramètres de propagation dans le point de vue clinique tel que la détection du travail et de prédiction du travail prématuré. Finalement, nous avons proposé une nouvelle combinaison entre Séparation Aveugles de Sources et la Décomposition en Modes Empiriques pour débruiter les signaux EHG monopolaires comme un moyen possible d'augmenter le taux de classification de signaux grossesse et l'accouchement

Analysis of the propagation of uterine electrical activity applied to predict preterm labor

There are many open questions concerning the functioning of the human uterus. One of these open questions concerns exactly how the uterus operates as an organ to perform the very organized act of contracting in a synchronized fashion to expulse a new human into this world. If we don‟t understand how it works when it is working normally, it is obvious that we will not be as capable of intervening or preventing when, sometimes with tragic consequences, it does not do its job properly and a child is born before it is ready. The aim of our research is to be able to understand what the electrical activity of the uterus can tell us about the risk of premature birth, to understand better how the uterus works and to benefit from these understanding to find tool that can be used for labor detection and prediction of preterm labor. This idea of using the externally detected electrical activity of the uterus (electrohysterogram or EHG) to predict preterm labor is not new and lot of work has already been put into it. The novel approach in this work is not to use the signal collected from one or two isolated places on the expectant mother‟s abdomen but to map the propagation of the signals and to investigate the auto organization of the contractions. We therefore use a matrix of electrodes to give us a much more complete picture of the organization and operation of the uterus as pregnancy reaches its conclusion. Labor is the physiologic process by which a fetus is expelled from the uterus to the outside world and is defined as regular uterine contractions accompanied by cervical effacement and dilatation. In the normal labor, the uterine contractions and cervix dilatation are preceded by biochemical changes in the cervical connective tissue.Il reste beaucoup de questions ouvertes concernant le fonctionnement de l'utérus humain. L'une de ces questions est comment l'utérus fonctionne en tant qu‟organe organisé pour générer une contraction synchrone et expulser un nouvel être humain dans ce monde ? Si nous ne comprenons pas comment l‟utérus fonctionne, quand il fonctionne normalement, il est évident que nous ne serons pas en mesure d'intervenir ou de prévoir quand, avec parfois des conséquences tragiques, il ne fait pas son travail correctement et qu‟un enfant nait avant d‟être prêt ! Le but de notre recherche est de comprendre ce que l'activité électrique de l'utérus peut nous apporter sur la prévention du risque de naissance prématurée, de mieux comprendre comment fonctionne l'utérus et de bénéficier de ces connaissances pour développer un outil qui peut être utilisé pour la détection de l‟accouchement et la prédiction du travail prématuré. Cette idée d'utiliser l'activité électrique détectée à la surface de l‟abdomen (ou électrohystérogramme EHG) pour prédire un accouchement prématuré n'est pas nouvelle et beaucoup de travaux ont déjà été mis en oeuvre. La nouvelle approche dans ce travail n‟est pas d‟utiliser le signal recueilli par un ou deux endroits isolés sur l'abdomen de la future mère, mais de cartographier la propagation des signaux et d‟explorer l'auto organisation des contractions. Nous utilisons donc une matrice d'électrodes pour nous donner une image beaucoup plus complète de l'organisation et du fonctionnement de l'utérus. L‟accouchement est le processus physiologique par lequel le foetus est expulsé de l'utérus vers le monde extérieur. Il est défini comme la survenue de contractions utérines régulières accompagnées de l'effacement du col et de la dilatation cervicale. Dans le travail normal, les contractions de l'utérus et la dilatation du col sont précédées par des changements biochimiques du tissu conjonctif du col utérin