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

Big data analysis of cyclic alternating pattern during sleep using deep learning

Sleep scoring has been of great interest since the invention of the polysomnography method, which enabled the recording of physiological signals overnight. With the surge in wearable devices in recent years, the topic of what is high-quality sleep, how can it be determined and how can it be achieved attracted increasing interest. In the last two decades, cyclic alternating pattern (CAP) was introduced as a scoring alternative to traditional sleep staging. CAP is known as a synonym for sleep microstructure and describes sleep instability. Manual CAP scoring performed by sleep experts is a very exhausting and time-consuming task. Hence, an automatic method would facilitate the processing of sleep data and provide a valuable tool to enhance the understanding of the role of CAP. This thesis aims to expand the knowledge about CAP by developing a high-performance automated CAP scoring system that can reliably detect and classify CAP events in sleep recordings. The automated system is equipped with state-of-the-art signal processing methods and exploits the dynamic, temporal information in brain activity using deep learning. The automated scoring system is validated using large community-based cohort studies and comparing the output to verified values in the literature. Our findings present novel clinical results on the relationship between CAP and age, gender, subjective sleep quality, and sleep disorders demonstrating that automated CAP analysis of large population based studies can lead to new findings on CAP and its subcomponents. Next, we study the relationship between CAP and behavioural, cognitive, and quality-of-life measures and the effect of adenotonsillectomy on CAP in children with obstructive sleep apnoea as the link between CAP and cognitive functioning in children is largely unknown. Finally, we investigate cortical-cardiovascular interactions during CAP to gain novel insights into the causal relationships between cortical and cardiovascular activity that are underpinning the microstructure of sleep. In summary, the research outcomes in this thesis outline the importance of a fully automated end-to-end CAP scoring solution for future studies on sleep microstructure. Furthermore, we present novel critical information for a better understanding of CAP and obtain first evidence on physiological network dynamics between the central nervous system and the cardiovascular system during CAP.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 202

Formation control of autonomous vehicles with emotion assessment

Autonomous driving is a major state-of-the-art step that has the potential to transform the mobility of individuals and goods fundamentally. Most developed autonomous ground vehicles (AGVs) aim to sense the surroundings and control the vehicle autonomously with limited or no driver intervention. However, humans are a vital part of such vehicle operations. Therefore, an approach to understanding human emotions and creating trust between humans and machines is necessary. This thesis proposes a novel approach for multiple AGVs, consisting of a formation controller and human emotion assessment for autonomous driving and collaboration. As the interaction between multiple AGVs is essential, the performance of two multi-robot control algorithms is analysed, and a platoon formation controller is proposed. On the other hand, as the interaction between AGVs and humans is equally essential to create trust between humans and AGVs, the human emotion assessment method is proposed and used as feedback to make autonomous decisions for AGVs. A novel simulation platform is developed for navigating multiple AGVs and testing controllers to realise this concept. Further to this simulation tool, a method is proposed to assess human emotion using the affective dimension model and physiological signals such as an electrocardiogram (ECG) and photoplethysmography (PPG). The experiments are carried out to verify that humans' felt arousal and valence levels could be measured and translated to different emotions for autonomous driving operations. A per-subject-based classification accuracy is statistically significant and validates the proposed emotion assessment method. Also, a simulation is conducted to verify AGVs' velocity control effect of different emotions on driving tasks

Desarrollo de nuevos dispositivos y algoritmos para la monitorización ambulatoria de personas con epilepsia

La epilepsia es una enfermedad crónica con un enorme impacto sociosanitario. Aunque en la actualidad se dispone de una gran cantidad de fármacos antiepilépticos y de otros tratamientos más selectivos como la cirugía o la estimulación cerebral, un porcentaje considerable de pacientes no están controlados y continúan teniendo crisis epilépticas. Estas personas suelen vivir condicionadas por la posibilidad de un ataque epiléptico y sus posibles consecuencias, como accidentes, lesiones o incluso la muerte súbita inexplicable. En este contexto, un dispositivo capaz de monitorizar el estado de salud y avisar de un posible ataque epiléptico contribuiría a mejorar la calidad de vida de estas personas. La presente Tesis Doctoral se centra en el desarrollo de un novedoso sistema de monitorización ambulatoria que permita identificar y predecir los ataques epilépticos. Dicho sistema está compuesto por diferentes sensores capaces de registrar de forma sincronizada diferentes señales biomédicas. Mediante técnicas de aprendizaje automático supervisado, se han desarrollado diferentes modelos predictivos capaces de clasificar el estado de la persona epiléptica en normal, preictal (antes de la crisis) e ictal (crisis)

Secondary Analysis of Electronic Health Records

Health Informatics; Ethics; Data Mining and Knowledge Discovery; Statistics for Life Sciences, Medicine, Health Science

Principal component based system identification and its application to the study of cardiovascular regulation

Includes bibliographical references (p. 197-212).Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2004.(cont.) Our methods analyze the coupling between instantaneous lung volume and heart rate and, subsequently, derive representative indices of parasympathetic and sympathetic control based on physiological and experimental findings. The validity of each method is evaluated via experimental data collected following interventions with known effect on the parasympathetic or sympathetic control. With the above techniques, this thesis explores an important topic in the field of space medicine: effects of simulated microgravity on cardiac autonomic control and orthostatic intolerance (OI). Experimental data from a prolonged bed rest study (simulation of microgravity condition) are analyzed and the conclusions are: 1) prolonged bed rest may impair autonomic control of heart rate; 2) orthostatic intolerance after bed rest is associated with impaired sympathetic responsiveness; 3) there may be a pre-bed rest predisposition to the development of OI after bed rest. These findings may have significance for studying Earth-bound orthostatic hypotension as well as for designing effective countermeasures to post-flight OI. In addition, they also indicate the efficacy of our proposed methods for autonomic function quantification.System identification is an effective approach for the quantitative study of physiologic systems. It deals with the problem of building mathematical models based on observed data and enables a dynamical characterization of the underlying physiologic mechanisms specific to the individual being studied. In this thesis, we develop and validate a new linear time-invariant system identification approach which is based on a weighted-principal component regression (WPCR) method. An important feature of this approach is its asymptotic frequency-selective property in solving time-domain parametric system identification problems. Owing to this property, data-specific candidate models can be built by considering the dominant frequency components inherent in the input (and output) signals, which is advantageous when the signals are colored, as are most physiologic signals. The efficacy of this method in modeling open-loop and closed-loop systems is demonstrated with respect to simulated and experimental data. In conjunction with the WPCR-based system identification approach, we propose new methods to noninvasively quantify cardiac autonomic control. Such quantification is important in understanding basic pathophysiological mechanisms or in patient monitoring, treatment design and follow-up.by Xinshu Xiao.Ph.D

Automation of the anesthetic process: New computer-based solutions to deal with the current frontiers in the assessment, modeling and control of anesthesia

The current trend in automating the anesthetic process focuses on developing a system for fully controlling the different variables involved in anesthesia. To this end, several challenges need to be addressed first. The main objective of this thesis is to propose new solutions that provide answers to the current problems in the field of assessing, modeling and controlling the anesthetic process. Undoubtedly, the main handicap to the development of a comprehensive proposal lies in the absence of a reliable measure of analgesia. This thesis proposes a novel fuzzy-logic-based scheme to evaluate the impact of including a new variable in a decision-making process. This scheme is validated by way of a preliminary analysis of the Analgesia Nociception Index (ANI) monitor on analgesic drug titration. Furthermore, the capacity of the ANI monitor to provide information to replicate the decisions of the experts in different clinical situations is studied. To this end, different artificial intelligence-based algorithms are used: specifically, the suitability of this index is evaluated against other variables commonly used in clinical practice. Regarding the modeling of anesthesia, this thesis presents an adaptive model that allows characterizing the pharmacological interaction effects between the hypnotic and analgesic drug on the depth of hypnosis. In addition, the proposed model takes into account both inter- and intra-patient variabilities observed in the response of the subjects. Finally, this work presents the synthesis of a robust optimal PID controller for regulating the depth of hypnosis by considering the effect of the uncertainties derived from the patient's pharmacological response. Moreover, a study is conducted on the limitations introduced when using a PID controller versus the development of higher order solutions under the same clinical and technical considerations