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

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

Evaluation of advanced artificial neural network classification and feature extraction techniques for detecting preterm births using ehg records

Globally, the rate of preterm births is increasing and this is resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. However, there has been some evidence to suggest 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 when preterm delivery is about to occur. Using advanced machine learning algorithms, in conjunction with electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. In this paper however, the electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset that contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilized, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. Seven artificial neural network algorithms are considered with the results showing that the Radial Basis Function Neural Network classifier performs the best, with 85% sensitivity, 80% specificity, 90% area under the curve and a 17% mean error rate. © 2014 Springer International Publishing Switzerland

Advanced Artificial Neural Network Classification for Detecting Preterm Births Using EHG Records

Globally, the rate of preterm births are increasing, thus resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. Nevertheless, there has 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 when preterm delivery is about to occur. Using advanced machine learning algorithms, in conjunction with Electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. However, in this paper, the Electrohysterography signals have been used to detect preterm births. This has been 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 have been utilized, as well as feature-ranking techniques. Features are ranked to determine their discriminative capabilities in detecting term and preterm records. Seven different artificial neural networks were then used to identify these records. The results illustrate that the Radial Basis Function Neural Network classifier performed the best, with 85% sensitivity, 80% specificity, 90% area under the curve and a 17% mean error rate

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%

The Utilisiation of composite Machine Learning models for the Classification of Medical Datasets For Sickle Cell Disease

The increase growth of health information systems has provided a significant way to deliver great change in medical domains. Up to this date, the majority of medical centres and hospitals continue to use manual approaches for determining the correct medication dosage for sickle cell disease. Such methods depend completely on the experience of medical consultants to determine accurate medication dosages, which can be slow to analyse, time consuming and stressful. The aim of this paper is to provide a robust approach to various applications of machine learning in medical domain problems. The initial case study addressed in this paper considers the classification of medication dosage levels for the treatment of sickle cell disease. This study base on different architectures of machine learning in order to maximise accuracy and performance. The leading motivation for such automated dosage analysis is to enable healthcare organisations to provide accurate therapy recommendations based on previous data. The results obtained from a range of models during our experiments have shown that a composite model, comprising a Neural Network learner, trained using the Levenberg-Marquardt algorithm, combined with a Random Forest learner, produced the best results when compared to other models with an Area under the Curve of 0.995

An analyses of the status of landfill classification systems in developing countries: Sub Saharan Africa landfill experiences

Municipal solid waste (MSW) management remains a challenge in developing countries due to increasing waste generation, high costs associated with waste management and the structure of the containment systems implemented. This study analyses the classification of landfilling systems by using documented cases reported mainly in publications in waste management in relation to non-engineered landfilling systems/approved dumpsites in Sub Saharan African (SSA) countries from 2000 to 2018. The work identifies an existing system for the classification of landfill sites and utilises this system to determine the situation of landfill sites in SSA countries. Each article was categorised according to the main landfilling management practice reported: Uncontrolled dumping, semi controlled facility, medium controlled facility, medium/high-engineered facility or high state-of the-art facility. Findings suggested that 80% of the documented cases of landfill sites assessed in SSA countries were classified as level 0 or 1. The structure of the containment and controlled regime were identified by the focus group discussion participants as important predictors of possible strengths, weaknesses, opportunities and threats for the landfill sites considered. The study represents the first identifiable and comprehensive academic evaluation of landfill site classification based on site operations reported in the available peer reviewed literature. The information provides insight on the status of landfill sites in SSA countries with respect to the landfilling management practice and a baseline for alternative corrective measures

Anthelmintic activity and non-cytotoxicity of phaeophorbide-a isolated from the leaf of Spondias mombin L.

ETHNOPHARMACOLOGICAL RELEVANCE: Helminthosis (worm infection) is a disease of grazing livestock, with significant economic implications. Increasing resistance to existing synthetic anthelmintics used to control helminthosis and the unwanted presence of residues of the anthelmintics reported in meat and dairy products present a serious global health challenge. These challenges have necessitated the development of novel anthelmintics that could combat drug resistance and exhibit better safety profiles. Spondias mombin L. (Anacardiaceae) is a plant that has been used traditionally as a worm expeller. AIM OF STUDY: The aim of the work reported herein was to isolate and characterise anthelmintic compound(s) from S. mombin leaf, establishing their bioactivity and safety profile. MATERIALS AND METHODS: Adult Haemonchus placei motility assay was used to assess anthelmintic bioactivity. Bioassay-guided chromatographic fractionation of acetone extract of S. mombin leaf was carried out on a silica gel stationary phase. The structure of the compound was elucidated using spectroscopy (1H and 13C NMR) and Liquid Chromatography-Mass Spectrometry (LC-ESI-MS). Screening to exclude potential cytotoxicity against mammalian cells (H460, Caco-2, MC3T3-E1) was done using alamar blue (AB) and CellTitreGlo (CTG) viability reagents. RESULTS: The acetone extract yielded an active fraction 8 (Ethyl acetate: methanol 90:10; anthelmintic LC50: 3.97 mg/mL), which yielded an active sub-fraction (Ethyl acetate: Methanol 95:5; anthelmintic LC50: 53.8 μg/mL), from which active compound 1 was isolated and identified as phaeophorbide-a (LC50: 23.0 μg/mL or 38.8 μM). The compound was not toxic below 200 μM but weakly cytotoxic at 200 μM. CONCLUSIONS: Phaeophorbide-a (1) isolated from S. mombin leaf extract and reported in the plant for the first time in this species demonstrated anthelmintic activity. No significant toxicity to mammalian cells was observed. It therefore represents a novel anthelmintic pharmacophore as a potential lead for the development of novel anthelmintics

RSM (Response Surface Methodology) Modelling of Inter-Electrodes Spacing Effects on Phosphate Removal

RSM modelling has been applied in this study to understand the effects of inter-electrodes on the performance of the electrochemical reactors in the removal of pollutants. RSM has been selected because it has the ability to predict the effects of more than one parameter on the targeted variable. Thus, the RSM has been used in this article to model the effects of inter-electrodes spaces (IES) (4 to 10 mm) and treatment time (TT) (5 – 55 min) on the ability of the electrocoagulation (EC) cells to remove phosphate from water. The results showed the best removal of phosphate was 92.5% at I-ES of 4 mm and TT of 50 min. High agreement was noticed between experimental and predicted removals (R 2 = 0.984)

Improving access to health care for malaria in Africa: a review of literature on what attracts patients

BACKGROUND: Increasing access to health care services is considered central to improving the health of populations. Existing reviews to understand factors affecting access to health care have focused on attributes of patients and their communities that act as 'barriers' to access, such as education level, financial and cultural factors. This review addresses the need to learn about provider characteristics that encourage patients to attend their health services. METHODS: This literature review aims to describe research that has identified characteristics that clients are looking for in the providers they approach for their health care needs, specifically for malaria in Africa. Keywords of 'malaria' and 'treatment seek*' or 'health seek*' and 'Africa' were searched for in the following databases: Web of Science, IBSS and Medline. Reviews of each paper were undertaken by two members of the team. Factors attracting patients according to each paper were listed and the strength of evidence was assessed by evaluating the methods used and the richness of descriptions of findings. RESULTS: A total of 97 papers fulfilled the inclusion criteria and were included in the review. The review of these papers identified several characteristics that were reported to attract patients to providers of all types, including lower cost of services, close proximity to patients, positive manner of providers, medicines that patients believe will cure them, and timeliness of services. Additional categories of factors were noted to attract patients to either higher or lower-level providers. The strength of evidence reviewed varied, with limitations observed in the use of methods utilizing pre-defined questions and the uncritical use of concepts such as 'quality', 'costs' and 'access'. Although most papers (90%) were published since the year 2000, most categories of attributes had been described in earlier papers. CONCLUSION: This paper argues that improving access to services requires attention to factors that will attract patients, and recommends that public services are improved in the specific aspects identified in this review. It also argues that research into access should expand its lens to consider provider characteristics more broadly, especially using methods that enable open responses. Access must be reconceptualized beyond the notion of barriers to consider attributes of attraction if patients are to receive quality care quickly