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

Autonomic control in preterm infants - what we can learn from mathematical descriptions of vital signs

Background: Preterm birth is a major burden, affecting approximately 15 million infants each year. Recent advances in reproductive medicine increases that number even more. The population of preterm infants in particular suffers from autonomic dysregulation that manifests as temperature instability and poor control of heart rate and breathing. Thermal care, monitoring of vital signs in a neonatal intensive care unit, pharmacotherapy, and respiratory support over weeks to months is necessary. Improvements in neonatal care in the past years lead to a decrease in mortality, especially in very preterm infants. However, former preterm infants still are a high-risk population for acute and chronic sequelae as a result of the interruption of the physiological development. A better understanding of the pathophysiology of the autonomic dysregulation in that population would be very useful. Unfortunately, accurate diagnostic tools that objectively assess and quantify the immature autonomic control in neonates are lacking. Methods: In this PhD thesis we examined different effects of the immature autonomic control in very preterm infants under clinically relevant conditions. We conducted a prospective single center observational study, where we assessed fluctuations in body temperature, sleep behavior, and heart rate variability in very preterm infants. We described the different regulatory systems using distinct mathematical expressions, such as detrended fluctuation analysis and sample entropy. We assessed associations between these outcome parameters and relevant factors of the infant’s history, such as demographic parameters and co-morbidities. Besides that, we analyzed lung function measurements of preterm infants and healthy term controls at a comparable postconceptional age, to describe respiratory control. Results: This study is systematically assessing different physiological signals of autonomic dysregulation in preterm infants during their first days of life. We found associations between parameters describing the complexity of time series analysis and maturity or relevant co-morbidities of the infants. In the analysis of heart rate variability we even found that parameters derived from time series analysis, assessed during the infants first days of life, improve our ability to predict future evolution of the infants’ autonomic stability. Lastly, several weeks after the expected due date, tidal breathing pattern of preterm infants showed a different reaction in response to a sigh when compared to term born controls at equivalent postmenstrual age indicating that autonomic dysregulation in preterm infants is still present well beyond the expected due date. Conclusion: A better understanding about the resolution of autonomic dysregulation in this population is not only important for the infant and its family but has the potential to support resource allocation and identification of patients with elevated risk for future deterioration. We thus think that the insights about the immature autonomic control in preterm infants, gained through this PhD work, are of substantial scientific and clinical relevance

Development of machine learning schemes for use in non-invasive and continuous patient health monitoring

Stephanie Baker developed machine learning schemes for the non-invasive and continuous measurement of blood pressure and respiratory rate from heart activity waveforms. She also constructed machine learning models for mortality risk assessment from vital sign variations. This research contributes several tools that offer significant advancements in patient monitoring and wearable healthcare

Morphological Variability Analysis of Physiologic Waveform for Prediction and Detection of Diseases

For many years it has been known that variability of the morphology of high-resolution (∼30-1000 Hz) physiological time series data provides additional prognostic value over lower resolution (≤ 1Hz) derived averages such as heart rate (HR), breathing rate (BR) and blood pressure (BP). However, the field has remained rather ad hoc, based on hand-crafted features. Using a model-based approach we explore the nature of these features and their sensitivity to variabilities introduced by changes in both the sampling period (HR) and observational reference frame (through breathing). HR and BR are determined as having a statistically significant confounding effect on the morphological variability (MV) evaluated in high-resolution physiological time series data, thus an important gap is identified in previous studies that ignored the effects of HR and BR when measuring MV. We build a best-in-class open-source toolbox for exploring MV that accounts for the confounding factors of HR and BR. We demonstrate the toolbox’s utility in three domains on three different signals: arterial BP in sepsis; photoplethysmogram in coarctation of the aorta; and electrocardiogram (ECG) in post-traumatic stress disorder (PTSD). In each of the three case studies, incorporating features that capture MV while controlling for BR and/or HR improved disease classification performance compared to previously established methods that used features from lower resolution time series data. Using the PTSD example, we then introduce a deep learning approach that significantly improves our ability to identify the effects of PTSD on ECG morphology. In particular, we show that pre-training the algorithm on a database of over 70,000 ECGs containing a set of 25 rhythms, allowed us to boost performance from an area under the receiver operating characteristic curve (AUROC) of 0.61 to 0.85. This novel approach to identifying morphology indicates that there is much more to morphological variability during stressful PTSD-related events than the simple periodic modulation of the T-wave amplitude. This research indicates that future work should focus on identifying the etiology of the dynamic features in the ECG that provided such a large boost in performance, since this may reveal novel underlying mechanisms of the influence of PTSD on the myocardium.Ph.D