Advanced analyses of physiological signals and their role in Neonatal Intensive Care

Preterm infants admitted to the neonatal intensive care unit (NICU) face an array of life-threatening diseases requiring procedures such as resuscitation and invasive monitoring, and other risks related to exposure to the hospital environment, all of which may have lifelong implications. This thesis examined a range of applications for advanced signal analyses in the NICU, from identifying of physiological patterns associated with neonatal outcomes, to evaluating the impact of certain treatments on physiological variability. Firstly, the thesis examined the potential to identify infants at risk of developing intraventricular haemorrhage, often interrelated with factors leading to preterm birth, mechanical ventilation, hypoxia and prolonged apnoeas. This thesis then characterised the cardiovascular impact of caffeine therapy which is often administered to prevent and treat apnoea of prematurity, finding greater pulse pressure variability and enhanced responsiveness of the autonomic nervous system. Cerebral autoregulation maintains cerebral blood flow despite fluctuations in arterial blood pressure and is an important consideration for preterm infants who are especially vulnerable to brain injury. Using various time and frequency domain correlation techniques, the thesis found acute changes in cerebral autoregulation of preterm infants following caffeine therapy. Nutrition in early life may also affect neurodevelopment and morbidity in later life. This thesis developed models for identifying malnutrition risk using anthropometry and near-infrared interactance features. This thesis has presented a range of ways in which advanced analyses including time series analysis, feature selection and model development can be applied to neonatal intensive care. There is a clear role for such analyses in early detection of clinical outcomes, characterising the effects of relevant treatments or pathologies and identifying infants at risk of later morbidity

Objective auditory brainstem response classification using machine learning

The objective of this study was to use machine learning in the form of a deep neural network to objectively classify paired auditory brainstem response waveforms into either: ‘clear response’, ‘inconclusive’ or ‘response absent’. A deep convolutional neural network was constructed and fine-tuned using stratified 10-fold cross-validation on 190 paired ABR waveforms. The final model was evaluated on a test set of 42 paired waveforms. The full dataset comprised 232 paired ABR waveforms recorded from eight normal-hearing individuals. The dataset was obtained from the PhysioBank database. The paired waveforms were independently labelled by two audiological scientists in order to train the network and evaluate its performance. The trained neural network was able to classify paired ABR waveforms with 92.9% accuracy. The sensitivity and the specificity were 92.9% and 96.4%, respectively. This neural network may have clinical utility in assisting clinicians with waveform classification for the purpose of hearing threshold estimation. Further evaluation using a large clinically obtained dataset would provide further validation with regard to the clinical potential of the neural network in diagnostic adult testing, newborn testing and in automated newborn hearing screening

PREDICTION OF SEPSIS DISEASE BY ARTIFICIAL NEURAL NETWORKS

Sepsis is a fatal condition, which affects at least 26 million people in the world every year that is resulted by an infection. For every 100,000 people, sepsis is seen in 149-240 of them and it has a mortality rate of 30%. The presence of infection in the patient is determined in order to diagnose the sepsis disease. Organ dysfunctions associated with an infection is diagnosed as sepsis. With the increased usage of artificial intelligence in the field of medicine, the early prediction and treatment of many diseases are provided with these methods. Considering the learning, reasoning and decision making abilities of artificial neural networks, which are the sub field of artificial intelligence are inferred to be used in predicting early stages of sepsis disease and determining the sepsis level is assessed. In this study, it is aimed to help sepsis diagnosis by using multi-layered artificial neural network.In construction of artificial neural network model, feed forward back propagation network structure and Levenberg-Marquardt training algorithm were used. The input and output variables of the model were the parameters which doctors use to diagnose the sepsis disease and determine the level of sepsis. The proposed method aims to provide an alternative prediction model for the early detection of sepsis disease

Clinical risk modelling with machine learning: adverse outcomes of pregnancy

As a complex biological process, there are various health issues that are related to pregnancy. Prenatal care, a type of preventative healthcare at different points in gestation is comprised of management, treatment, and mitigation of such issues. This also includes risk prediction for adverse pregnancy outcomes, where probabilistic modelling is used to calculate individual’s risk at the early stages of pregnancy. This type of modelling can have a definite clinical scope such as in prenatal screening, and an educational aim where awareness of a healthy lifestyle is promoted, such as in health education. Currently, the most used models are based on traditional statistical approaches, as they provide sufficient predictive power and are easily interpreted by clinicians. Machine learning, a subfield of data science, contains methods for building probabilistic models with multidimensional data. Compared to existing prediction models related to prenatal care, machine learning models can provide better results by fitting more intricate nonlinear decision boundary areas, improve data-driven model fitting by generating synthetic data, and by providing more automation for routine model adjustment processes. This thesis presents the evaluation of machine learning methods to prenatal screening and health education prediction problems, along with novel methods for generating synthetic rare disorder data to be used for modelling, and an adaptive system for continuously adjusting a prediction model to the changing patient population. This way the thesis addresses all the four main entities related to predicting adverse outcomes of pregnancy: the mother or patient, the clinician, the screening laboratory and the developer or manufacturer of screening materials and systems.Kliinisen riskin mallinnus koneoppimismenetelmin: raskaudelle haitalliset lopputulemat Raskaus on kompleksinen biologinen prosessi, jonka etenemiseen liittyy useita terveysongelmia. Äitiyshoito voidaan kuvata ennalta ehkäiseväksi terveydenhuolloksi, jossa pyritään käsittelemään, hoitamaan ja lievittämään kyseisiä ongelmia. Tähän hoitoon sisältyy myös raskauden haitallisten lopputulemien riskilaskenta, missä probabilistista mallinnusta hyödynnetään määrittämään yksilön riski raskauden varhaisissa vaiheissa. Tällä mallinnuksella voi olla selkeä kliininen tarkoitus kuten prenataaliseulonta, tai terveyssivistyksellinen tarkoitus missä odottavalle äidille esitellään raskauden kannalta terveellisiä elämäntapoja. Tällä hetkellä eniten käytössä olevat ennustemallit perustuvat perinteiseen tilastolliseen mallinnukseen, sille ne tarjoavat riittävän ennustetehokkuuden ja ovat helposti tulkittavissa. Koneoppiminen on datatieteen osa-alue, joka pitää sisällään menetelmiä millä voidaan mallintaa moniulotteista dataa ennustekäyttöön. Verrattuna olemassa oleviin äitiyshoidon ennustemalleihin, koneoppiminen mahdollistaa parempien ennustetulosten tuottamisen sovittamalla hienojakoisempia epälineaarisia päätösalueita, tehostamalla datakeskeisten mallien sovitusta luomalla synteettisiä havaintoja ja tarjoamalla enemmän automaatiota rutiininomaiseen mallien hienosäätöön. Tämä väitös esittelee koneoppimismenetelmien evaluaation prenataaliseulonta-ja terveyssivistysongelmiin, ja uusia menetelmiä harvinaisten sairauksien datan luomiseen mallinnustarkoituksiin ja jatkuvan ennustemallin hienosäätämisen järjestelmän muuttuvia potilaspopulaatiota varten. Näin väitös käy läpi kaikki neljä asianomaista jotka liittyvät haitallisten lopputulemien ennustamiseen: odottava äiti eli potilas, kliinikko, seulontalaboratorio ja seulonnassa käytettävien materiaalien ja järjestelmien kehittäjä tai valmistaja

Characterization of Early Cortical Neural Network Development in Multiwell Microelectrode Array Plates.

We examined neural network ontogeny using microelectrode array (MEA) recordings made in multiwell MEA (mwMEA) plates over the first 12 days in vitro (DIV). In primary cortical cultures, action potential spiking activity developed rapidly between DIV 5 and 12. Spiking was sporadic and unorganized at early DIV, and became progressively more organized with time, with bursting parameters, synchrony, and network bursting increasing between DIV 5 and 12. We selected 12 features to describe network activity; principal components analysis using these features demonstrated segregation of data by age at both the well and plate levels. Using random forest classifiers and support vector machines, we demonstrated that four features (coefficient of variation [CV] of within-burst interspike interval, CV of interburst interval, network spike rate, and burst rate) could predict the age of each well recording with >65% accuracy. When restricting the classification to a binary decision, accuracy improved to as high as 95%. Further, we present a novel resampling approach to determine the number of wells needed for comparing different treatments. Overall, these results demonstrate that network development on mwMEA plates is similar to development in single-well MEAs. The increased throughput of mwMEAs will facilitate screening drugs, chemicals, or disease states for effects on neurodevelopment.EC was supported by a Wellcome Trust PhD studentship and NIHR Cambridge Biomedical Research Centre studentship. DH was supported by student services contract #EP-13-D-000108 and by a travelling fellowship from the Company of Biologists.This is the final version of the article. It first appeared from SAGE Publications via https://doi.org/10.1177/108705711664052

Seizure Detection, Seizure Prediction, and Closed-Loop Warning Systems in Epilepsy

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy

A Process for Extracting Knowledge in Design for the Developing World

The aim of this study was to develop the process necessary to identify design knowledge shared across product classes and contexts in Design for the Developing World. A process for extracting design knowledge in the field of Design for the Developing World was developed based on the Knowledge Discovery in Databases framework. This process was applied to extract knowledge from a sample dataset of 48 products and small-scale technologies. Unsupervised cluster analysis revealed two distinct product groups, cluster X-AA and cluster Z-AC-AD. Unique attributes of cluster XX-AA include local manufacture, local maintenance and service, human-power, distribution by a non-governmental organization, income-generation, and application in water/sanitation or agriculture sectors. The label Locally Oriented Design for the Developing World was assigned to this group based on the dominant features represented. Unique attributes of cluster Z-AC-AD include electric-power, distribution by a private organization, and application in the health or energy/communication sectors. The label Globally Oriented Design for the Developing World was assigned to this group. These findings were corroborated by additional analyses that suggest certain design knowledge is shared across classes and contexts within groups of products. The results suggest that at least two of these groups exist, which can serve as an initial framework for organizing the literature related to inter-context and inter-class design knowledge. Design knowledge was extracted from each group by collecting known approaches, principles, and methods from available literature. This knowledge may be applied as design guidance in future work by identifying a product group corresponding to the design scenario and sourcing the related set of knowledge