Uterine contractions clustering based on surface electromyography: an input for pregnancy monitoring

Tese de mestrado em Bioestatística, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, em 2018Inicialmente a investigação da contratilidade uterina recorria à utilização de dois métodos: o tocograma externo e o cateter de pressão intrauterino. Ambos os métodos apresentam limitações ao nível da avaliação do risco de parto prematuro e na monitorização da gravidez. O EHG (Electrohisterograma) é um método alternativo ao tocograma externo e ao cateter de pressão intrauterino. Este método pode ser aplicado de forma invasiva no músculo uterino, ou de forma não invasiva através de elétrodos colocados no abdómen. O EHG tem sido considerado uma ferramenta adequada para a monitorização da gravidez e do parto. O índice de massa corporal tem um impacto quase impercetível no EHG, sendo esta uma das principais características deste método. O EHG pode também ser utilizado para identificar as mulheres que vão entrar em trabalho de parto e ainda auxiliar na tomada de decisão médica quanto à utilização da terapia tocolítica (antagonista da oxitocina), evitando deste modo a ingestão de medicação desnecessária e os consequentes efeitos secundários. Na literatura existem apenas cinco casos publicados em que foi realizada uma separação dos principais eventos do sinal EHG: contrações, movimentos fetais, ondas Alvarez e ondas LDBF (Longue Durée Basse Fréquence). Em três das publicações a separação dos eventos foi feita manualmente e nos restantes casos algoritmos, como redes neuronais, foram aplicados ao EHG. As ondas Alvarez e as Braxton-Hicks são as mais reconhecidas. As ondas Alvarez foram descritas pela primeira vez nos anos cinquenta e as Braxton-Hicks foram descritas pela primeira vez em 1872 sendo detetadas através de palpação. As ondas Alvarez são ocasionalmente sentidas pela mulher. Estas ondas estão localizadas numa pequena área do tecido uterino sem propagação e podem levar a contrações com maior intensidade e, consequentemente, ao parto pré-termo. As Braxton-Hicks são contrações ineficientes registadas a partir da 20ª semana de gravidez que se tornam mais frequentes e intensas com o decorrer da gravidez. Estas contrações são menos localizadas que as ondas Alvarez e, durante o parto, propagam-se por todo o tecido uterino num curto período de tempo. As Braxton-Hicks estão associadas a uma diminuição do ritmo cardíaco fetal. As ondas LDBF são contrações de longa duração associadas a hipertonia uterina, quando há contração do tecido uterino sem retorno ao relaxamento muscular, o que representa um risco na gravidez. Neste trabalho foram utilizadas duas bases de dados. Na base de dados da Islândia existem 122 registos de 45 mulheres, dos quais apenas 4 correspondem a partos pré-termo. Na base de dados TPEHG (Term-Preterm EHG) existem 300 registos, dos quais 38 correspondem a partos pré-termo. Neste trabalho foram escolhidos canais bipolares, visto que estes reduzem o ruído idêntico, como o ECG (Eletrocardiograma) materno ou movimentos respiratórios. Para ambas as bases de dados os sinais originais de EHG foram processados e filtrados. Na estimação espetral foram considerados dois métodos: paramétricos e não paramétricos. O método Welch foi escolhido pois representa um bom compromisso entre ambos. Este método foi utilizado para calcular o espectro de cada evento detetado no sinal EHG. Para detetar os eventos no sinal EHG foram considerados cinco métodos baseados na energia ou amplitude. O método Wavelet foi o escolhido pois após uma inspeção visual, este era o método que delineava melhor as contrações. Na base de dados da Islândia foram identificadas 3136 contrações e na TPEHG foram encontradas 4622 contrações. O objetivo principal desta tese é obter clusters de contrações detetadas no sinal EHG. No entanto, as contrações são séries temporais não estacionárias, e a sua classificação visual é inviável a longo termo e também difícil de aplicar na prática clínica. Existem vários parâmetros que podem ser extraídos do sinal EHG, mas o espectro das contrações foi o método escolhido visto que este representa o sinal EHG e tem sempre a mesma dimensão, independentemente da duração da contração. As distâncias espetrais têm sido utilizadas com sucesso no reconhecimento áudio. Neste trabalho foi realizada uma aplicação desse método ao processamento do EHG, no qual foram realizados os ajustes necessários. Para comparar os espectros foram estudadas 8 distâncias diferentes: Itakura-Saito, COSH, Itakura, Itakura simétrica, Kullback-Leibler, Jeffrey, Rényi e Jensen-Rényi. Apenas as distâncias simétricas foram selecionadas para um estudo mais detalhado visto que estas são, segundo a literatura, as distâncias mais adequadas aquando do clustering. Após comparação das distâncias simétricas, a divergência de Jeffrey foi a selecionada para a comparação dos espectros. Nesta tese foram avaliados três métodos diferentes de clustering: o linkage, o K-means e o K-medoids. O linkage é um método hierárquico. Os clusters que resultam do agrupamento hierárquico estão organizados numa estrutura chamada dendrograma. No agrupamento hierárquico, não é necessário predeterminar o número de clusters, o que torna este um método ideal na exploração dos dados. O K-means e o K-medoids são métodos de partição, nos quais os dados são separados em k clusters decididos previamente. Os clusters são definidos de forma a otimizar a função da distância. No algoritmo K-means, os clusters baseiam-se na proximidade entre si de acordo com uma distância predeterminada. A diferença entre o K-medoids e o K-means é que o K-medoids escolhe pontos de dados como centros, chamados de medoides, enquanto K-means usa centróides. Após uma comparação dos diferentes métodos de clustering foi escolhido neste trabalho foi o average linkage, visto que este apresentava melhores resultados quer na separação dos espectros quer na silhueta. É então apresentado um método inovador no qual se utiliza todo o espectro das contrações detetadas automaticamente no EHG para o clustering não supervisionado. Esta técnica é uma contribuição para a classificação automática das diferentes contrações, especialmente aquelas mais reconhecidas na literatura: Alvarez e Braxton-Hicks. Era expectável encontrar um cluster isolado com as ondas LDBF, visto que estas representam um risco para o feto. O principal objetivo era juntar num cluster os espectros semelhantes das contrações, e relacioná-lo com o respetivo tipo de contração. Essa tarefa foi concluída através da identificação positiva de Alvarez e Braxton-Hicks. O clustering forneceu ainda algumas pistas sobre ondas Alvarez que não foram encontradas com o algoritmo de deteção de contrações, situação para a qual um método alternativo é apresentado. É sugerido que as ondas Alvarez sejam detetadas com métodos baseados na frequência, como, por exemplo, a frequência instantânea, no entanto este método não foi desenvolvido neste trabalho. Em relação às ondas LDBF, estas foram encontradas no cluster das Braxton-Hicks. É sugerido que a deteção das ondas LDBF seja baseada na sua caraterística mais distinta: a longa duração. Verificou-se que os casos pré-termo e os registos pré-parto não ficaram isolados num cluster, não se tendo encontrado uma relação entre a idade gestacional e o tipo de contração. Conclui-se que as contrações mais curtas apresentam maior amplitude do que as contrações com maior duração. Baseado em estudos anteriores sobre a eletrofisiologia do útero, supõem-se que o início do trabalho de parto pré-termo e termo esteja associado a sequências específicas de diferentes tipos de contrações, nas quais as ondas Alvares desempenham um papel importante. As contrações identificadas como Alvarez e Braxton-Hicks não são usadas como tal na prática clínica apesar de a maioria das contrações detetadas pelo tocograma serem Braxton-Hicks. O interesse pelas ondas Alvarez diminuiu rapidamente visto que estas ondas são praticamente indetetáveis pelo método de referência de deteção de contrações: o tocograma. As capacidades e a resolução do EHG levaram à renovação do estudo das contrações mais subtis, incluindo as Alvarez. Este trabalho é uma contribuição para a investigação nesta área.An innovative technique is introduced wherein where an unsupervised clustering method using as feature the whole spectrum of automatically detected contractions on the EHG (Electrohysterogram) is presented as a contribution to the automatic classification of the different uterine contractions, at least those that have been most recognized in the literature: Alvarez and Braxton-Hicks. It was expected to also be able to cluster the LDBF (Longue Durée Basse Fréquence) components, as these pose a fetal risk. The main task was to have the spectral contractions descriptions clustered and linked to the respective contraction type. That task was completed with positive identification of the Alvarez and Braxton-Hicks. The clustering process also provided clues regarding the missed Alvarez waves in the contraction detection algorithm, for which an alternative technique is suggested but not developed in this work. Regarding the LDBF they were found in the Braxton-Hicks cluster. It is suggested the LDBF´s to be detected based in their most prominent feature: the long duration. It is presented the rationale behind the selection of a cost function to be used in the spectral distance’s algorithm. Spectral distances have been successfully used in audio recognition and this works represents an application to the EHG processing, for which the necessary adjustments have to be implemented. It was found that no single cluster pointed to the preterm cases, or indeed to the pre-labor subject recordings. It is hypothesized, based on previous studies in uterine electrophysiology, that the initiation of pre-term or term labor should be associated with triggering contraction sequences of different types, where the Alvarez waves play a major role. Alvarez and Braxton-Hicks, labeled as such, are not typically used in the clinical environment despite most of the Tocogram detected contractions being the latter. Alvarez waves are not usually detectable by the Tocogram. Alvarez were firstly detected invasively in the early fifties, and Braxton-Hicks in 1872 using routine palpation techniques. The interest in Alvarez components declined rapidly since being practically undetectable by the de facto reference in the contraction detection: the Tocogram. The EHG capabilities and resolution made it possible to revive the research on the most subtle uterine contractions, Alvarez included and this work is a contribution in this research area

Unsupervised Classification of Uterine Contractions Recorded Using Electrohysterography

Pregnancy still poses health risks that are not attended to by current clinical practice motorization procedures. Electrohysterography (EHG) record signals are analyzed in the course of this thesis as a contribution and effort to evaluate their suitability for pregnancy monitoring. The presented work is a contributes with an unsupervised classification solution for uterine contractile segments to FCT’s Uterine Explorer (UEX) project, which explores analysis procedures for EHG records. In a first part, applied processing procedures are presented and a brief exploration of the best practices for these. The procedures include those to elevate the representation of uterine events relevant characteristics, ease further computation requirements, extraction of contractile segments and spectral estimation. More detail is put into the study of which characteristics should be chosen to represent uterine events in the classification process and feature selection methods. To such end, it is presented the application of a principal component analysis (PCA) to three sets: interpolated contractile events, contractions power spectral densities, and to a number of computed features that attempt evidencing time, spectral and non-linear characteristics usually used in EHG related studies. Subsequently, a wrapper model approach is presented as a mean to optimize the feature set through cyclically attempting the removal and re-addition of features based on clustering results. This approach takes advantage of the fact that one class is known beforehand to use its classification accuracy as the criteria that defines whether the modification made to the feature set was ominous. Furthermore, this work also includes the implementation of a visualization tool that allows inspecting the effect of each processing procedure, the uterine events detected by different methods and clusters they were associated to by the final iteration of the wrapper model

Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants is most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. There is a strong body of evidence emerging that suggests the analysis of uterine electrical signals, from the abdominal surface (Electrohysterography – EHG), could provide a viable way of diagnosing true labour and even predict preterm deliveries. This paper explores this idea further and presents a new dynamic self-organized network immune algorithm that classifies term and preterm records, using an open dataset containing 300 records (38 preterm and 262 term). Using the dataset, oversampling and cross validation techniques are evaluated against other similar studies. The proposed approach shows an improvement on existing studies with 89% sensitivity, 91% specificity, 90% positive predicted value, 90% negative predicted value, and an overall accuracy of 90%

Automatic contraction detection using uterine electromyography

UIDB/00066/2020 UID/MAT/04561/2019 PD/BDE/150312/2019Electrohysterography (EHG) is a promising technique for pregnancy monitoring and preterm risk evaluation. It allows for uterine contraction monitoring as early as the 20th gestational week, and it is a non-invasive technique based on recording the electric signal of the uterine muscle activity from electrodes located in the abdominal surface. In this work, EHG-based contraction detection methodologies are applied using signal envelope features. Automatic contraction detection is an important step for the development of unsupervised pregnancy monitoring systems based on EHG. The exploratory methodologies include wavelet energy, Teager energy, root mean square (RMS), squared RMS, and Hilbert envelope. In this work, two main features were evaluated: contraction detection and its related delineation accuracy. The squared RMS produced the best contraction (97.15 ± 4.66%) and delineation (89.43 ± 8.10%) accuracy and the lowest false positive rate (0.63%). Despite the wavelet energy method having a contraction accuracy (92.28%) below the first-rated method, its standard deviation was the second best (6.66%). The average false positive rate ranged between 0.63% and 4.74%—a remarkably low value.publishersversionpublishe

Electrohysterography in the diagnosis of preterm birth: a review

This is an author-created, un-copyedited versíon of an article published in Physiological Measurement. IOP Publishing Ltd is not responsíble for any errors or omissíons in this versíon of the manuscript or any versíon derived from it. The Versíon of Record is available online at http://doi.org/10.1088/1361-6579/aaad56.[EN] Preterm birth (PTB) is one of the most common and serious complications in pregnancy. About 15 million preterm neonates are born every year, with ratios of 10-15% of total births. In industrialized countries, preterm delivery is responsible for 70% of mortality and 75% of morbidity in the neonatal period. Diagnostic means for its timely risk assessment are lacking and the underlying physiological mechanisms are unclear. Surface recording of the uterine myoelectrical activity (electrohysterogram, EHG) has emerged as a better uterine dynamics monitoring technique than traditional surface pressure recordings and provides information on the condition of uterine muscle in different obstetrical scenarios with emphasis on predicting preterm deliveries. Objective: A comprehensive review of the literature was performed on studies related to the use of the electrohysterogram in the PTB context. Approach: This review presents and discusses the results according to the different types of parameter (temporal and spectral, non-linear and bivariate) used for EHG characterization. Main results: Electrohysterogram analysis reveals that the uterine electrophysiological changes that precede spontaneous preterm labor are associated with contractions of more intensity, higher frequency content, faster and more organized propagated activity and stronger coupling of different uterine areas. Temporal, spectral, non-linear and bivariate EHG analyses therefore provide useful and complementary information. Classificatory techniques of different types and varying complexity have been developed to diagnose PTB. The information derived from these different types of EHG parameters, either individually or in combination, is able to provide more accurate predictions of PTB than current clinical methods. However, in order to extend EHG to clinical applications, the recording set-up should be simplified, be less intrusive and more robust-and signal analysis should be automated without requiring much supervision and yield physiologically interpretable results. Significance: This review provides a general background to PTB and describes how EHG can be used to better understand its underlying physiological mechanisms and improve its prediction. The findings will help future research workers to decide the most appropriate EHG features to be used in their analyses and facilitate future clinical EHG applications in order to improve PTB prediction.This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under grant DPI2015-68397-R.Garcia-Casado, J.; Ye Lin, Y.; Prats-Boluda, G.; Mas-Cabo, J.; Alberola Rubio, J.; Perales Marin, AJ. (2018). Electrohysterography in the diagnosis of preterm birth: a review. Physiological Measurement. 39(2). https://doi.org/10.1088/1361-6579/aaad56S39

Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

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