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

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

Unified approach to catastrophic events: from the normal state to geological or biological shock in terms of spectral fractal and nonlinear analysis

An important question in geophysics is whether earthquakes (EQs) can be anticipated prior to their occurrence. Pre-seismic electromagnetic (EM) emissions provide a promising window through which the dynamics of EQ preparation can be investigated. However, the existence of precursory features in pre-seismic EM emissions is still debatable: in principle, it is difficult to prove associations between events separated in time, such as EQs and their EM precursors. The scope of this paper is the investigation of the pre-seismic EM activity in terms of complexity. A basic reason for our interest in complexity is the striking similarity in behavior close to irreversible phase transitions among systems that are otherwise quite different in nature. Interestingly, theoretical studies (Hopfield, 1994; Herz and Hopfield 1995; Rundle et al., 1995; Corral et al., 1997) suggest that the EQ dynamics at the final stage and neural seizure dynamics should have many similar features and can be analyzed within similar mathematical frameworks. Motivated by this hypothesis, we evaluate the capability of linear and non-linear techniques to extract common features from brain electrical activities and pre-seismic EM emissions predictive of epileptic seizures and EQs respectively. The results suggest that a unified theory may exist for the ways in which firing neurons and opening cracks organize themselves to produce a large crisis, while the preparation of an epileptic shock or a large EQ can be studied in terms of ''Intermittent Criticality''

Visual Representation Learning with Minimal Supervision

Computer vision intends to provide the human abilities of understanding and interpreting the visual surroundings to computers. An essential element to comprehend the environment is to extract relevant information from complex visual data so that the desired task can be solved. For instance, to distinguish cats from dogs the feature 'body shape' is more relevant than 'eye color' or the 'amount of legs'. In traditional computer vision it is conventional to develop handcrafted functions that extract specific low-level features such as edges from visual data. However, in order to solve a particular task satisfactorily we require a combination of several features. Thus, the approach of traditional computer vision has the disadvantage that whenever a new task is addressed, a developer needs to manually specify all the features the computer should look for. For that reason, recent works have primarily focused on developing new algorithms that teach the computer to autonomously detect relevant and task-specific features. Deep learning has been particularly successful for that matter. In deep learning, artificial neural networks automatically learn to extract informative features directly from visual data. The majority of developed deep learning strategies require a dataset with annotations which indicate the solution of the desired task. The main bottleneck is that creating such a dataset is very tedious and time-intensive considering that every sample needs to be annotated manually. This thesis presents new techniques that attempt to keep the amount of human supervision to a minimum while still reaching satisfactory performances on various visual understanding tasks. In particular, this thesis focuses on self-supervised learning algorithms that train a neural network on a surrogate task where no human supervision is required. We create an artificial supervisory signal by breaking the order of visual patterns and asking the network to recover the original structure. Besides demonstrating the abilities of our model on common computer vision tasks such as action recognition, we additionally apply our model to biomedical scenarios. Many research projects in medicine involve profuse manual processes that extend the duration of developing successful treatments. Taking the example of analyzing the motor function of neurologically impaired patients we show that our self-supervised method can help to automate tedious, visually based processes in medical research. In order to perform a detailed analysis of motor behavior and, thus, provide a suitable treatment, it is important to discover and identify the negatively affected movements. Therefore, we propose a magnification tool that can detect and enhance subtle changes in motor function including motor behavior differences across individuals. In this way, our automatic diagnostic system does not only analyze apparent behavior but also facilitates the perception and discovery of impaired movements. Learning a feature representation without requiring annotations significantly reduces human supervision. However, using annotated dataset leads generally to better performances in contrast to self-supervised learning methods. Hence, we additionally examine semi-supervised approaches which efficiently combine few annotated samples with large unlabeled datasets. Consequently, semi-supervised learning represents a good trade-off between annotation time and accuracy

Biology of GDNF and its receptors — Relevance for disorders of the central nervous system

a b s t r a c t a r t i c l e i n f o Article history: Received 7 December 2015 Revised 14 January 2016 Accepted 25 January 2016 Available online xxxx A targeted effort to identify novel neurotrophic factors for midbrain dopaminergic neurons resulted in the isolation of GDNF (glial cell line-derived neurotrophic factor) from the supernatant of a rat glial cell line in 1993. Over two decades and 1200 papers later, the GDNF ligand family and their different receptor systems are now recognized as one of the major neurotrophic networks in the nervous system, important for the development, maintenance and function of a variety of neurons and glial cells. The many ways in which the four members of the GDNF ligand family can signal and function allow these factors to take part in the control of multiple types of processes, from neuronal survival to axon guidance and synapse formation in the developing nervous system, to synaptic function and regenerative responses in the adult. In this review, we will briefly summarize basic aspects of GDNF signaling mechanisms and receptor systems and then review our current knowledge of the physiology of GDNF activities in the central nervous system, with an eye to its relevance for neurodegenerative and neuropsychiatric diseases