90 research outputs found

    Prediction of short-term health outcomes in preterm neonates from heart-rate variability and blood pressure using boosted decision trees

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    Background and Objective: Efficient management of low blood pressure (BP) in preterm neonates remains challenging with considerable variability in clinical practice. There is currently no clear consensus on what constitutes a limit for low BP that is a risk to the preterm brain. It is argued that a personalised approach rather than a population based threshold is more appropriate. This work aims to assist healthcare professionals in assessing preterm wellbeing during episodes of low BP in order to decide when and whether hypotension treatment should be initiated. In particular, the study investigates the relationship between heart rate variability (HRV) and BP in preterm infants and its relevance to a short-term health outcome. Methods: The study is performed on a large clinically collected dataset of 831 h from 23 preterm infants of less than 32 weeks gestational age. The statistical predictive power of common HRV features is first assessed with respect to the outcome. A decision support system, based on boosted decision trees (XGboost), was developed to continuously estimate the probability of neonatal morbidity based on the feature vector of HRV characteristics and the mean arterial blood pressure. Results: It is shown that the predictive power of the extracted features improves when observed during episodes of hypotension. A single best HRV feature achieves an AUC of 0.87. Combining multiple HRV features extracted during hypotensive episodes with the classifier achieves an AUC of 0.97, using a leave-one-patient-out performance assessment. Finally it is shown that good performance can even be achieved using continuous HRV recordings, rather than only focusing on hypotensive events – this had the benefit of not requiring invasive BP monitoring. Conclusions: The work presents a promising step towards the use of multimodal data in providing objective decision support for the prediction of short-term outcome in preterm infants with hypotensive episodes

    Intelligent monitoring and interpretation of preterm physiological signals using machine learning

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    Every year, more than one in ten babies are born prematurely. In Ireland of the 70000 babies delivered every year, 4500 are born too early. Premature babies are at a higher risk of complications, which may lead to both short-term and long-term adverse health outcomes. The neonatal population is especially vulnerable and a delay in the identification of medical conditions, as well as delays in the initiating the correct treatment, may be fatal. After birth, preterms are admitted to the neonatal intensive care unit (NICU), where a continuous flow of information in the form of physiological signals is available. Physiological signals can assist clinicians in decision making related to the diagnosis and treatment of various diseases. This information, however, can be highly complex, and usually requires expert analysis which may not be available at all times. The work conducted in this thesis develops a decision support systems for the intelligent monitoring of preterms in the NICU. This will allow for an accurate estimation of the current health status of the preterm neonate as well as the prediction of possible long-term complications. This thesis is comprised of three main work packages (WP), each addressing health complication of preterm on three different stages of life. At the first 12 hours of life the health status is quantified using the clinical risk index for babies (CRIB). This is followed by the assessment of the preterm’s well-being at discharge from the NICU using the clinical course score (CCS). Finally, the long-term neurodevelopmental follow-up is assessed using the Bayley III scales of development at two years. This is schematically represented in Figure 1 along with the main findings and contributions. Low blood pressure (BP) or hypotension is a recognised problem in preterm infants particularly during the first 72 hours of life. Hypotension may cause decreased cerebral perfusion, resulting in deprived oxygen delivery to the brain. Deciding when and whether to treat hypotension relies on our understanding of the relation between BP, oxygenation and brain activity. The electroencephalogram (EEG) is the most commonly used technology to assess the ‘brain health’ of a newborn. The first WP investigates the relationship between short-term dynamics in BP and EEG energy in the preterm on a large dataset of continuous multi-channel unedited EEG recordings in the context of the health status measured by the CRIB score. The obtained results indicate that a higher risk of mortality for the preterm is associated with a lower level of nonlinear interaction between EEG and BP. The level of coupling between these two systems can potentially serve as an additional source of information when deciding whether or not to intervene in the preterm. The electrocardiogram (ECG) is also routinely recorded in preterm infants. Analysis of heart rate variability (HRV) provides a non-invasive assessment of both the sympathetic and parasympathetic control of the heart rate. A novel automated objective decision support tool for the prediction of the short-term outcome (CCS) in preterm neonates who may have low BP is proposed in the second WP. Combining multiple HRV features extracted during hypotensive episodes, the classifier achieved an AUC of 0.97 for the task of short-term outcome prediction, using a leave-one-patient-out performance assessment. The developed system is based on the boosted decision tree classifier and allows for the continuous monitoring of the preterm. The proposed system is validated on a large clinically collected dataset of multimodal recordings from preterm neonates. If the correct treatment is initiated promptly after diagnosis, it can potentially improve the neurodevelopmental outcome of the preterm infant. The third WP presents a pilot study investigating the predictive capability of the early EEG recorded at discharge from the NICU with respect to the 2-year neurodevelopmental outcome using machine learning techniques. Two methods are used: 1) classical feature-based classifier, and 2) end-to-end deep learning. This is a fundamental study in this area, especially in the context of applying end-to-end learning to the preterm EEG for the problem of long-term outcome prediction. It is shown that for the available labelled dataset of 37 preterm neonates, the classical feature-based approach outperformed the end-to-end deep learning technique. A discussion of the obtained result as well as a section highlighting the possible limitations and areas that need to be investigated in the future are provided

    Heart rate variability during periods of low blood pressure as a predictor of short-term outcome in preterms

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    Efficient management of low blood pressure (BP) in preterm neonates remains challenging with a considerable variability in clinical practice. The ability to assess preterm wellbeing during episodes of low BP will help to decide when and whether hypotension treatment should be initiated. This work aims to investigate the relationship between heart rate variability (HRV), BP and the short-term neurological outcome in preterm infants less than 32 weeks gestational age (GA). The predictive power of common HRV features with respect to the outcome is assessed and shown to improve when HRV is observed during episodes of low mean arterial pressure (MAP) - with a single best feature leading to an AUC of 0.87. Combining multiple features with a boosted decision tree classifier achieves an AUC of 0.97. The work presents a promising step towards the use of multimodal data in building an objective decision support tool for clinical prediction of short-term outcome in preterms who suffer episodes of low BP

    ASSESSMENT OF RISK IN PRETERM INFANTS USING POINT PROCESS AND MACHINE LEARNING APPROACHES

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    Preemies, infants who are born too soon, have a higher incidence of Life-Threatening Events (LTE’s) such as apnea (cessation of breathing), bradycardia (slowing of heart rate) and hypoxemia (oxygen desaturation) also termed as ABD (Apnea, Bradycardia, and Desaturation) events. Clinicians at Neonatal Intensive Care Units (NICU) are facing the demanding task of assessing the risk of infants based on their physiological signals. The aim of this thesis is to develop a risk stratification algorithm using a machine-learning framework with the features related to pathological fluctuations derived from point process model that will be embedded into the current physiological recording system to assess the risk of life-threatening events well in advance of occurrence in individual infants in the NICU. We initially propose a point process algorithm of heart rate dynamics for risk stratification of preterm infants. Based on this analysis, point process indices were tested to determine whether they were useful as precursors for life-threatening events. Finally, a machine-learning framework using point process indices as precursors were designed and tested to classify the risk of preterm infants. This work helps to predict the number of bradycardia events, N, in the subsequent hours measuring point process indices for the current hour. The model proposed uses Quadratic Support Vector Machine (QSVM), a machine learning classifier, which can solve class optimization problems and execute data at an exponential speed with higher accuracy for risk assessment that might facilitate effective management and treatment for preterm infants in NICU. The findings are relevant to risk assessment by analyzing the fluctuations in physiological signals that can act as precursors for the future life-threatening events

    On the automated analysis of preterm infant sleep states from electrocardiography

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    On the automated analysis of preterm infant sleep states from electrocardiography

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    Classification of Foetal Distress and Hypoxia Using Machine Learning

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    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

    Video-based infant discomfort detection

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