Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine

<p>Abstract</p> <p>Background</p> <p>Cardiotocography (CTG) is the most widely used tool for fetal surveillance. The visual analysis of fetal heart rate (FHR) traces largely depends on the expertise and experience of the clinician involved. Several approaches have been proposed for the effective interpretation of FHR. In this paper, a new approach for FHR feature extraction based on empirical mode decomposition (EMD) is proposed, which was used along with support vector machine (SVM) for the classification of FHR recordings as 'normal' or 'at risk'.</p> <p>Methods</p> <p>The FHR were recorded from 15 subjects at a sampling rate of 4 Hz and a dataset consisting of 90 randomly selected records of 20 minutes duration was formed from these. All records were labelled as 'normal' or 'at risk' by two experienced obstetricians. A training set was formed by 60 records, the remaining 30 left as the testing set. The standard deviations of the EMD components are input as features to a support vector machine (SVM) to classify FHR samples.</p> <p>Results</p> <p>For the training set, a five-fold cross validation test resulted in an accuracy of 86% whereas the overall geometric mean of sensitivity and specificity was 94.8%. The Kappa value for the training set was .923. Application of the proposed method to the testing set (30 records) resulted in a geometric mean of 81.5%. The Kappa value for the testing set was .684.</p> <p>Conclusions</p> <p>Based on the overall performance of the system it can be stated that the proposed methodology is a promising new approach for the feature extraction and classification of FHR signals.</p

THE INFLUENCE OF CARDIOTOCOGRAM SIGNAL FEATURE SELECTION METHOD ON FETAL STATE ASSESSMENT EFFICACY

Cardiotocographic (CTG) monitoring is a method of assessing fetal state. Since visual analysis of CTG signal is difficult, methods of automated qualitative fetal state evaluation on the basis of the quantitative description of the signal are applied. The appropriate selection of learning data influences the quality of the fetal state assessment with computational intelligence methods. In the presented work we examined three different feature selection procedures based on: principal components analysis, receiver operating characteristics and guidelines of International Federation of Gynecology and Obstetrics. To investigate their influence on the fetal state assessment quality the benchmark SisPorto dataset and the Lagrangian support vector machine were used

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors

Analysis of Dimensionality Reduction Techniques on Big Data

Due to digitization, a huge volume of data is being generated across several sectors such as healthcare, production, sales, IoT devices, Web, organizations. Machine learning algorithms are used to uncover patterns among the attributes of this data. Hence, they can be used to make predictions that can be used by medical practitioners and people at managerial level to make executive decisions. Not all the attributes in the datasets generated are important for training the machine learning algorithms. Some attributes might be irrelevant and some might not affect the outcome of the prediction. Ignoring or removing these irrelevant or less important attributes reduces the burden on machine learning algorithms. In this work two of the prominent dimensionality reduction techniques, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are investigated on four popular Machine Learning (ML) algorithms, Decision Tree Induction, Support Vector Machine (SVM), Naive Bayes Classifier and Random Forest Classifier using publicly available Cardiotocography (CTG) dataset from University of California and Irvine Machine Learning Repository. The experimentation results prove that PCA outperforms LDA in all the measures. Also, the performance of the classifiers, Decision Tree, Random Forest examined is not affected much by using PCA and LDA.To further analyze the performance of PCA and LDA the eperimentation is carried out on Diabetic Retinopathy (DR) and Intrusion Detection System (IDS) datasets. Experimentation results prove that ML algorithms with PCA produce better results when dimensionality of the datasets is high. When dimensionality of datasets is low it is observed that the ML algorithms without dimensionality reduction yields better results

Towards Sustainable Urban Futures: Exploring Environmental Initiatives in Smart Cities

Environmentally sustainable smart cities have emerged as a promising approach to address the challenges of urbanization while promoting sustainable development and enhancing residents' quality of life. This research article presents the key findings of a comprehensive study that explores the various aspects and initiatives found in environmentally sustainable smart cities.Renewable energy plays a pivotal role in these cities, with a strong emphasis on harnessing solar, wind, and geothermal power. Investments in clean energy infrastructure, such as solar panels, wind farms, and geothermal plants, significantly reduce reliance on fossil fuels and contribute to lower carbon emissions.Energy efficiency is another critical aspect of sustainable smart cities. These cities prioritize the use of smart grids for optimized energy distribution, smart meters for real-time energy monitoring and control, and energy-efficient buildings equipped with insulation, lighting, and HVAC systems that minimize energy consumption.Smart transportation is a key initiative in environmentally sustainable smart cities, focusing on reducing traffic congestion and air pollution. Electric vehicles (EVs) are promoted, accompanied by the development of charging infrastructure. Intelligent transportation systems aid in effective traffic management, while active transportation modes such as cycling, walking, and public transportation are encouraged.Efficient waste management systems are implemented to minimize landfill waste and promote recycling and composting. Smart waste bins equipped with sensors optimize waste collection routes, reduce littering, and provide real-time data on fill levels, aiding in effective waste management.Water management strategies are prioritized to conserve this precious resource. Smart water meters monitor consumption patterns, rainwater harvesting systems are implemented, water-efficient practices are promoted in buildings, and advanced leak detection technologies minimize water loss.Green spaces and biodiversity conservation are fundamental in environmentally sustainable smart cities. By integrating parks, gardens, rooftop greenery, and urban forests, these cities enhance residents' well-being, improve air quality, and provide habitats for wildlife, thus promoting biodiversity.Data analytics and the Internet of Things (IoT) play a crucial role in monitoring and optimizing various city systems. Real-time data collection and analysis enable effective management of energy usage, traffic flow, waste management, and other infrastructure, facilitating informed decision-making and resource allocation.Citizen engagement is fostered in environmentally sustainable smart cities. Platforms for citizen participation enable residents to provide feedback, report issues, and actively contribute to decision-making processes related to urban planning, energy conservation, waste management, and other sustainability initiatives.The implementation of these strategies in environmentally sustainable smart cities aims to reduce carbon footprints, enhance resource efficiency, improve air and water quality, and create healthier and more livable urban environments. By embracing technology, innovation, and citizen engagement, these cities pave the way for a sustainable and resilient future

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes

Gynaecologists and obstetricians visually interpret cardiotocography (CTG) traces using the International Federation of Gynaecology and Obstetrics (FIGO) guidelines to assess the wellbeing of the foetus during antenatal care. This approach has raised concerns among professionals with regards to inter- and intra-variability where clinical diagnosis only has a 30\% positive predictive value when classifying pathological outcomes. Machine learning models, trained with FIGO and other user derived features extracted from CTG traces, have been shown to increase positive predictive capacity and minimise variability. This is only possible however when class distributions are equal which is rarely the case in clinical trials where case-control observations are heavily skewed in favour of normal outcomes. Classes can be balanced using either synthetic data derived from resampled case training data or by decreasing the number of control instances. However, this either introduces bias or removes valuable information. Concerns have also been raised regarding machine learning studies and their reliance on manually handcrafted features. While this has led to some interesting results, deriving an optimal set of features is considered to be an art as well as a science and is often an empirical and time consuming process. In this paper, we address both of these issues and propose a novel CTG analysis methodology that a) splits CTG time-series signals into n-size windows with equal class distributions, and b) automatically extracts features from time-series windows using a one dimensional convolutional neural network (1DCNN) and multilayer perceptron (MLP) ensemble. Collectively, the proposed approach normally distributes classes and removes the need to handcrafted features from CTG traces

Smart Cities, Healthy Citizens: Integrating Urban Public Health in Urban Planning

Urban planning that incorporates public health considerations is crucial for the development of smart cities that prioritize the well-being and health of their citizens. This study presents key findings on integrating urban public health into urban planning to create environments that promote physical and mental well-being. The study identifies and explores several crucial considerations for achieving this integration.The first consideration is healthy urban design, which involves designing urban spaces and infrastructure that promote physical activity, accessibility, and safety. Walkable neighborhoods, well-connected sidewalks, bike lanes, and efficient public transit systems encourage active transportation. Incorporating parks, green spaces, and recreational facilities provide opportunities for exercise and outdoor activities, while inclusive and accessible public spaces reduce pollution and noise.Air quality and pollution control emerge as another vital consideration. The study highlights the importance of implementing policies to mitigate air pollution, reduce emissions, and promote clean energy sources. Designing urban areas to minimize exposure to pollution sources, increasing green spaces and urban forests, and utilizing smart technologies for monitoring air quality are key strategies for improving air quality and mitigating the heat island effect.Ensuring accessible healthcare and services is essential for equitable public health. The research emphasizes the need to strategically locate healthcare facilities to serve both urban and underserved areas. Attention should be given to the needs of vulnerable populations, such as the elderly, low-income communities, and individuals with disabilities. The integration of telemedicine and digital health solutions can enhance access to healthcare services.Promoting active transportation and safety is crucial in urban planning. The study highlights the importance of pedestrian and cyclist safety through well-designed crosswalks, traffic calming measures, and lighting systems. Dedicated cycling infrastructure, traffic management strategies, and smart traffic systems contribute to reducing accidents and improving road safety.Noise pollution management is an often overlooked aspect of urban planning. The research emphasizes the significance of designing buildings with sound insulation and implementing zoning regulations to separate noise-sensitive areas from noise-generating activities. Green buffers and sound barriers are effective in mitigating noise impacts, while monitoring noise levels and enforcing regulations minimize excessive noise.The study also underscores the importance of integrating elements that promote mental health and social well-being into urban planning. Creating inclusive and socially connected neighborhoods, designing public spaces that encourage socialization and relaxation, and prioritizing the provision of community centers and social services all contribute to mental health and well-being.Data and technology integration play a crucial role in informing urban planning decisions and improving public health outcomes. The study highlights the value of collecting and analyzing health-related data to identify health disparities, understand the impact of the built environment on health, and guide decision-making processes. Utilizing smart technologies, such as wearable devices and health monitoring systems, promotes individual health awareness and facilitates targeted interventions.Evaluation and monitoring are essential components of successful urban planning. Continuously monitoring and evaluating the impact of urban planning decisions on public health outcomes, collecting data on health indicators, and using this information to assess intervention effectiveness and inform future planning efforts are critical for sustainable development.Integrating urban public health considerations into urban planning enables the creation of smart and healthy environments that support the well-being of citizens. This holistic approach ensures that urban development fosters economic growth, technological advancement, and the health and happiness of the people who live and work in these cities

Classification of Caesarean Section and Normal Vaginal Deliveries Using Foetal Heart Rate Signals and Advanced Machine Learning Algorithms

ABSTRACT – Background: Visual inspection of Cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. Methodology: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤7.05 and pathological risk). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. Results: The findings show that deep learning classification achieves Sensitivity = 94%, Specificity = 91%, Area under the Curve = 99%, F-Score = 100%, and Mean Square Error = 1%. Conclusions: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies

Dynamical Systems of the BCM Learning Rule: Emergent Properties and Application to Clustering

The BCM learning rule has been used extensively to model how neurons in the brain cortex respond to stimulus. One reason for the popularity of the BCM learning rule is that, unlike its predecessors which use static thresholds to modulate neuronal activity, the BCM learning rule incorporates a dynamic threshold that serves as a homeostasis mechanism, thereby providing a larger regime of stability. This dissertation explores the properties of the BCM learning rule – as a dynamical system– in different time-scale parametric regimes. The main observation is that, under certain stimulus conditions, when homeostasis is at least as fast as synapse, the dynamical system undergoes bifurcations and may trade stability for oscillations, torus dynamics, and chaos. Analytically, it is shown that the conditions for stability are a function of the homeostasis time-scale parameter and the angle between the stimuli coming into the neuron. When the learning rule achieves stability, the BCM neuron becomes selective. This means that it exhibits high-response activities to certain stimuli and very low-response activities to others. With data points as stimuli, this dissertation shows how this property of the BCM learning rule can be used to perform data clustering analysis. The advantages and limitations of this approach are discussed, in comparison to a few other clustering algorithms