Advance Artificial Neural Network Classification Techniques Using EHG for Detecting Preterm Births

Worldwide the rate of preterm birth is increasing, which presents significant health, developmental and economic problems. Current methods for predicting preterm births at an early stage are inadequate. Yet, there has been increasing evidence that the analysis of uterine electrical signals, from the abdominal surface, could provide an independent and easy way to diagnose true labour and predict preterm delivery. This analysis provides a heavy focus on the use of advanced machine learning techniques and Electrohysterography (EHG) signal processing. Most EHG studies have focused on true labour detection, in the window of around seven days before labour. However, this paper focuses on using such EHG signals to detect preterm births. In achieving this, the study uses an open dataset containing 262 records for women who delivered at term and 38 who delivered prematurely. The synthetic minority oversampling technique is utilized to overcome the issue with imbalanced datasets to produce a dataset containing 262 term records and 262 preterm records. Six different artificial neural networks were used to detect term and preterm records. The results show that the best performing classifier was the LMNC with 96% sensitivity, 92% specificity, 95% AUC and 6% mean error

A critical look at studies applying over-sampling on the TPEHGDB dataset

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set

Training Neural networks for Experimental models: Classifying Biomedical Datasets for Sickle Cell Disease

This paper presents the use of various type of neural network architectures for the classification of medical data. Extensive research has indicated that neural networks generate significant improvements when used for the pre-processing of medical time-series data signals and have assisted in obtaining high accuracy in the classification of medical data. Up to date, most of hospitals and healthcare sectors in the United Kingdom are using manual approach for analysing patient input for sickle cell disease, which depends on clinician’s experience that can lead to time consuming and stress to patents. The results obtained from a range of models during our experiments have shown that the proposed Back-propagation trained feed-forward neural network classifier generated significantly better outcomes over the other range of classifiers. Using the ROC curve, experiments results showed the following outcomes for our models, in order of best to worst: Back-propagation trained feed-forward neural net classifier: 0.989, Functional Link neural Network: 0.972, in comparison to the Radial basis neural Network Classifiers with areas of 0.875, and the Voted Perception classifier: 0.766. A Linear Neural Network was used as baseline classifier to illustrate the importance of the previous models, producing an area of 0.849, followed by a random guessing model with an area of 0.524

Artificial Intelligence for Detecting Preterm Uterine Activity in Gynacology and Obstertric Care

Preterm birth brings considerable emotional and economic costs to families and society. However, despite extensive research into understanding the risk factors, the prediction of patient mechanisms and improvements to obstetrical practice, the UK National Health Service still annually spends more than £2.95 billion on this issue. Diagnosis of labour in normal pregnancies is important for minimizing unnecessary hospitalisations, interventions and expenses. Moreover, accurate identification of spontaneous preterm labour would also allow clinicians to start necessary treatments early in women with true labour and avert unnecessary treatment and hospitalisation for women who are simply having preterm contractions, but who are not in true labour. In this research, the Electrohysterography signals have been used to detect preterm births, because Electrohysterography signals provide a strong basis for objective prediction and diagnosis of preterm birth. This has been achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Three different machine learning algorithm were used to identify these records. The results illustrate that the Random Forest performed the best of sensitivity 97%, specificity of 85%, Area under the Receiver Operator curve (AUROC) of 94% and mean square error rate of 14%

Predicting and Visualising City Noise Levels to Support Tinnitus Sufferers

On a daily basis, urban residents are unconsciously exposed to hazardous noise levels. This has a detrimental effect on the ear-drum, with symptoms often not apparent till later in life. The impact of harmful noises levels has a damaging impact on wellbeing. It is estimated that 10 million people suffer from damaged hearing in the UK alone, with 6.4million of retirement age or above. With this number expected to increase significantly by 2031, the demand and cost for healthcare providers is expected to intensify. Tinnitus affects about 10 percent of the UK population, with the condition ranging from mild to severe. The effects can have psychological impact on the patient. Often communication becomes difficult, and the sufferer may also be unable to use a hearing aid due to buzzing, ringing or monotonous sounds in the ear. Action on Hearing Loss states that sufferers of hearing related illnesses are more likely to withdraw from social activities. Tinnitus sufferers are known to avoid noisy environments and busy urban areas, as exposure to excessive noise levels exacerbates the symptoms. In this paper, an approach for evaluating and predicting urban noise levels is put forward. The system performs a data classification process to identify and predict harmful noise areas at diverse periods. The goal is to provide Tinnitus sufferers with a real-time tool, which can be used as a guide to find quieter routes to work; identify harmful areas to avoid or predict when noise levels on certain roads will be dangerous to the ear-drum. Our system also performs a visualisation function, which overlays real-time noise levels onto an interactive 3D map. Keywords: Hazardous Noise Levels, Data Classification, Tinnitus, Visualisation, Hearing Loss, Prediction, Real-Tim

Uterine EMG Signals Spectral Analysis for Pre-Term Birth Prediction

A methodology for prediction of pre-term births is presented in this paper. The methodology is based on the analysis of EHG signals and data mining techniques. Initially, spectral and non-linear characteristics of the EHG are extracted, forming a pattern that is used to train a classifier to discriminate between term and pre-term cases. The method has been tested using a benchmark EHG database, and the obtained results indicate its effectiveness in accurate pre-term/term labour prediction

Preterm labor prediction using uterine electromyography with Machine Learning and Deep Learning Models

Trabalho de Projeto de Mestrado, Bioestatística, 2023, Universidade de Lisboa, Faculdade de CiênciasDe acordo com a Organização Mundial da Saúde (OMS) o parto prematuro é definido como o nascimento de bebés antes da finalização das 37 semanas de gestação, sendo considerado um risco de saúde elevado tanto para o bebé como para a mãe. Dois terços destes partos, não tem um diagnóstico específico, enquanto os restantes encontram-se normalmente associados a fatores relacionados com a mãe como várias gravidezes, historial de partos prematuros, uso de drogas, idade inferior a 18 anos, entre outros. A prematuridade é a primeira causa de morte no mundo para crianças com menos de 5 anos, uma vez que quando ocorre o parto, os bebés não se encontram completamente desenvolvidos, podendo vir a sofrer deficiências a nível visual e auditivo e também outras complicações ao nível da saúde como problemas cardiovasculares ou respiratórios. Em Portugal, de acordo com a Sociedade Portuguesa de Pediatria, 8% dos bebés nascem prematuros. Deste modo, a monitorização dos partos de forma a prever partos pré-termo tornou-se fundamental. Os dois métodos mais comumente usados na monitorização da contratilidade uterina são o Cateter de Pressão Intrauterino e o Tocograma Externo, porém ambos apresentam limitações como o facto de ser invasivo ou de não mostrar eficácia para grávidas de elevada massa corporal, respetivamente. O estudo da atividade das contrações no útero através do Electrohisterograma (EHG) como método alternativo tem sido uma forte aposta na previsão do parto prematuro. O EHG é um método não invasivo realizado através de elétrodos colocados no abdómen, que regista a atividade contrátil do útero e resulta num sinal elétrico. Demonstra eficácia em pacientes com índice de massa corporal alta, sendo capaz de indicar quando as grávidas vão entrar em trabalho de parto. Atualmente, o estudo do sinal EHG é uma das práticas mais usadas para estudar e classificar o parto prematuro através de técnicas de Machine Learning (ML) e Deep Learning (DL). Para isso, utilizam-se características frequenciais, temporais, entre outras provenientes do sinal, chamadas de features, que vão representar o sinal. Estas são depois inseridas em algoritmos de ML e DL capazes de fazer previsões com base nas características do sinal. Em literatura as features mais utilizadas para representar os sinais EHG consistem na frequência, amplitude, entropia e outras, demonstrando resultados positivos com elevado valor preditivo, tanto em algoritmos de Machine Learning como de Deep Learning. Desta forma, através do sinal EHG obtido na monitorização do útero será possível prever se a grávida irá ter um parto prematuro ou termo. No entanto, esta classificação ainda se encontra numa fase experimental, existindo uma lacuna no contexto clínico, para uma previsão automática do tipo de parto. Todos estes trabalhos enfrentam um problema associado à falta de observações de partos prematuros nas bases de dados utilizadas. As soluções propostas para combater o desequilíbrio nos dados envolve a utilização de técnicas de sobreamostragrem, como SMOTE, que consistem na produção de observações sintéticos para a classe da minoria (partos prematuros). O número ideal de amostras a serem produzidas é ainda algo a ser estudado, sendo que a maior parte dos estudos fazem uma compensação dos dados com uma proporção final de observações de 1:1, porém este método pode levar a um decréscimo na habilidade do classificador identificar a classe maioritária e uma previsão irrealista e demasiado otimista. De acordo com os autores, o SMOTE atinge os melhores resultados através da combinação de uma subamostragem da classe maioritária com a sobreamostragem da classe minoritária, através do SMOTE. Num sinal EHG processado é possível distinguir a existência de contrações como Braxton-Hicks, ondas Alvarez e ondas LDBF (Longue Durée Basse Fréquence). De momento, na literatura as features são extraídas do sinal completo e não das contrações, nomeadamente das Alvarez e Braxton-Hicks, que contêm informação relevante para a prematuridade do parto. Contudo, as contrações são séries temporais com um número diferente de observações. Deste modo, a solução apresentada para este problema é a análise espectral de cada contração, através do espetro de cada contração, obtido através de uma transformação de tempo para frequência, como a Transformada de Fourier, que é capaz de representar um sinal na base de dados. Esta técnica é usada para extração de features e classificação no campo de diagnóstico médico. Dentro da estimação espetral existem dois métodos: paramétricos e não paramétricos, sendo que o método Welch é uma abordagem não paramétrica, capaz de calcular o espetro de cada contração detetada no sinal EHG, que demonstrou bons resultados na classificação das contrações noutros trabalhos, representando bem o singal EHG, e apresentando sempre a mesma dimensão, independente da duração da contração. Neste estudo, foi utilizada a base de dados pública TPEHG (Term Preterm EHG) com um total de 300 registos, 262 pré-termo e 38 termo. A base de dados apresenta 4 elétrodos, com 3 canais bipolares, sendo que apenas um canal foi escolhido, de acordo com a literatura, visto que o sinal vertical tem uma maior variação do potencial de sinal. Este sinal foi depois filtrado para eliminar o ruído materno do ECG, ou outros ruídos relacionados, e processado para uma frequência amostral final de 4 Hz. As features foram extraídas através da estimação espetral pelo método Welch, finalizando com um total de 200 features. No final, o base de dados utilizado consistia em 4622 observações/contrações, 407 correspondentes a parto prematuro e 2829 parto termo, com 200 features cada. Esta base de dados foi depois fornecida a três algoritmos diferentes de ML, incluindo o Random Forest, RUSBoosted Trees, Support Vector Machine, e uma Shallow Neural Network, e o algoritmo Long-Short Term Memory de DL, com o objetivo de classificar os parto prematuros. Até agora, nenhum estudo se focou na utilização de um algoritmo de LSTM, e na utilização do espetro das contrações como features. Neste estudo, as técnicas mencionadas anteriormente foram aplicadas em 5 cenários diferentes nos algoritmos de ML, de modo a obter o modelo mais robusto para evitar situações de overfitting, e obter os resultados mais realistas possíveis, (1) treinar os dados, sem qualquer opção adicional de outros métodos; (2) treinar os dados com os mesmos algoritmos, adicionando uma técnica de sobreamostragem sintética, SMOTE; (3) treinar os dados com técnica de SMOTE mais uma técnica de redução de dimensionalidade, PCA; (4) treinar os dados com a utilização de um método de seleção de features, MRMR; (5) tuning dos parâmetros do modelo, através do método Bayesian Optimization. Desta forma, os dados foram treinados, validados, e os modelos com melhores resultados preditivos foram depois testados. Os algoritmos de DL foram apenas testados usando o dataset original e o dataset com SMOTE aplicado. Para todos os algoritmos, a accuracy, precision, recall, F1-Score, false negative rate, false positive rate e AUC (exceto para os de DL) foram calculados. Os resultados indicam que usar os primeiros 200 pontos da estimação espetral pelo método Welch, como features frequenciais, não proporciona melhores resultados quando comparando a features mais tradicionais, de tempo-frequência, usadas em toda a literatura. Além disso, utilizar a técnica de SMOTE conciliada com uma subamostragem da classe maioritária produz piores resultados quando comparando com a aplicação de só SMOTE, como usado pela maioria dos autores. Os algoritmos de ML têm um melhor comportamento que os de DL, uma vez que são modelos mais simples não dependentes de uma elevada quantidade de dados. Apesar dos resultados promissores no grupo de treino, com uma elevada Accuracy, F1-score e AUC, o momento de teste teve uma performance abaixo dos valores esperados e em literatura. Com base nestes resultados, concluímos que apesar da abordagem da aplicação de SMOTE após a separação em grupo de treino de teste ser a mais correta, não permite resultados semelhantes à literatura (em que esta ordem de passos usada é a inversa), uma vez que o algoritmo é processado usando um grupo de teste com uma estrutura muito diferente à de treino, o que pode levar a menor precision e recall. Em suma, conclui-se que a utilização do espetro das contrações como features frequenciais num dataset sobreamostrado com a técnica de SMOTE, utilizando as diferentes técnicas de ML e DL referidas, não é uma melhor alternativa em relação à utilização de features de tempo-frequência presentes em literatura. Contudo, é possível concluir a importância de registar mais dados de partos prematuros de EHG, com vista a melhorar as experiências futuras, e evitar a utilização de técnicas como a de SMOTE. Para além disso, abriu-se também a possibilidade da aplicação de uma rede neuronal complexa como o LSTM, com resultados promissores para o futuro, que podem ser eficazes quando aplicados na classificação de parto prematuro.The World Health Organization defines premature birth as the birth of a baby before the completion of 37 weeks of gestation which is considered a high health risk for both the baby and the mother. Prematurity is the leading cause of death in the world for children under 5 years old, therefore monitoring the uterus to predict preterm labor has become essential. Currently, the Intrauterine Pressure Catheter and the External Tocography are the most used monitoring devices, however, they are invasive and don’t perform well with high body mass index (BMI) patients, respectively. The Electrohysterogram (EHG) has emerged as a noninvasive method for predicting premature birth with high performance for mothers with high BMI. This method uses electrodes placed on the abdomen to record uterine contractions by producing an electrical signal, that contains important information regarding the electrical activity of the uterus. The study of the EHG signal is one of the most used practices for studying and classifying premature birth using Machine Learning (ML) and Deep Learning (DL) techniques. In this technique, features are extracted from the signal such as frequency, amplitude, and others to represent the signal and inserted into algorithms capable of making predictions based on the signal characteristics. However, this classification method is still in the experimental phase, and there is a gap in the clinical context for automatic birth type prediction. One of the challenges faced by this method is the lack of observations of premature births in the databases used. Oversampling techniques, such as SMOTE, address the lack of observations of premature births in the databases by producing synthetic observations for the minority class. In this thesis, the Welch estimation of the power spectra of the signal of each contraction from the TPEHG Ljubljana public database is used as features, comprising 200 features. The Minimum Redundancy Maximum Relevance (MRMR) Algorithm was used to search for the most relevant features from this dataset with only 180 showing any relevance, and SMOTE was applied to solve the skewed dataset problem. Four different machine learning algorithms were used, including the Support Vector Machine, the RUSBoosted trees, a Shallow Neural Network, and a Random Forest classifier, moreover, a deep learning network was also tested. These were also optimized with the Bayesian hyperparameter optimization. All algorithms performed with high accuracy, although showing a low predictive power for the test group, probably due to a highly imbalanced test set. We concluded that the use of spectral features of the contractions as an alternative to the timefrequency features shows promising results with the training dataset, but cannot accurately predict preterm labor in the test set, due to the imbalanced dataset problem. More samples should be collected in the future so more meaningful conclusions can be taken

Classification of Foetal Distress and Hypoxia Using Machine Learning

Foetal distress and hypoxia (oxygen deprivation) is considered a serious condition and one of the main factors for caesarean section in the obstetrics and gynaecology department. It is considered to be the third most common cause of death in new-born babies. Foetal distress occurs in about 1 in 20 pregnancies. Many foetuses that experience some sort of hypoxic effects can have series risks such as damage to the cells of the central nervous system that may lead to life-long disability (cerebral palsy) or even death. Continuous labour monitoring is essential to observe foetal wellbeing during labour. Many studies have used data from foetal surveillance by monitoring the foetal heart rate with a cardiotocography, which has succeeded traditional methods for foetal monitoring since 1960. Despite the indication of normal results, these results are not reassuring, and a small proportion of these foetuses are actually hypoxic. This study investigates the use of machine learning classifiers for classification of foetal hypoxic cases using a novel method, in which we are not only considering the classification performance only, but also investigating the worth of each participating parameter to the classification as seen by medical literature. The main parameters that are included in this study as indicators of metabolic acidosis are: pH level (which is a measure of the hydrogen ion concentration of blood to specify the acidity or alkalinity), as an indicator of respiratory acidosis; Base Deficit of extra-cellular fluid level and Base Excess (BE) (which is the measure of the total concentration of blood buffer base that indicates metabolic acidosis or compensated respiratory alkalosis). In addition to other parameters such as the PCO2 (partial pressure of carbon dioxide can reflect the hypoxic state of the foetus) and the Apgar scores (which shows the foetal physical activity within a specific time interval after birth). The provided data was an open-source partum clinical data obtained by Physionet, including both hypoxic cases and normal cases. Six well known machine learning classifier are used for the classification; each model was presented with a set of selected features derived from the clinical data. Classifier evaluation is performed using the receiver operating characteristic curve analysis, area under the curve plots, as well as confusion matrix. The simulation results indicate that machine learning classifiers provide good results in diagnosis of foetal hypoxia, in addition to acceptable results of different combinations of parameters to differentiate the cases

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