Prediction of Sudden Cardiac Death Using Ensemble Classifiers

Sudden Cardiac Death (SCD) is a medical problem that is responsible for over 300,000 deaths per year in the United States and millions worldwide. SCD is defined as death occurring from within one hour of the onset of acute symptoms, an unwitnessed death in the absence of pre-existing progressive circulatory failures or other causes of deaths, or death during attempted resuscitation. Sudden death due to cardiac reasons is a leading cause of death among Congestive Heart Failure (CHF) patients. The use of Electronic Medical Records (EMR) systems has made a wealth of medical data available for research and analysis. Supervised machine learning methods have been successfully used for medical diagnosis. Ensemble classifiers are known to achieve better prediction accuracy than its constituent base classifiers. In an effort to understand the factors contributing to SCD, data on 2,521 patients were collected for the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT). The data included 96 features that were gathered over a period of 5 years. The goal of this dissertation was to develop a model that could accurately predict SCD based on available features. The prediction model used the Cox proportional hazards model as a score and then used the ExtraTreesClassifier algorithm as a boosting mechanism to create the ensemble. We tested the system at prediction points of 180 days and 365 days. Our best results were at 180-days with accuracy of 0.9624, specificity of 0.9915, and F1 score of 0.9607

Conception de système de traitement de données sur les émotions d’un être humain dans un environnement mobile et incertain

Une émotion humaine est considérée comme un état d’esprit d’un individu qui est complexe et intense, débutant de manière brutale et peut durer pendant une période relativement brève. Les émotions affectent généralement à la fois l’état physiologique et psychologique et peuvent aider à améliorer la santé humaine et l’efficacité au travail si elles sont positives, tandis que les émotions négatives peuvent causer des problèmes de santé et de comportements très graves. La détection et la surveillance des émotions sont primordiales dans de nombreux domaines tels que la conduite de véhicules, afin d’agir dans le temps opportun en cas de présence d’un état émotionnel négatif qui peut affecter dangereusement la vie du conducteur. Dans la science, on a défini plusieurs méthodes de détection d’émotions qui peuvent être classifiées en deux grandes catégories ; l’une utilise les signaux physiques humains tels que l’expression faciale, la parole, le geste, la posture, etc., qui ont l’avantage d’être facilement collectés et étudiés, mais qui souffrent d’une fiabilité modeste en raison de la possibilité de ne pas montrer les signaux physiques vrais pour cacher de véritables émotions. La deuxième catégorie utilise les signaux internes (les signaux physiologiques), qui comprennent l’électrocardiogramme (ECG), l’électroencéphalogramme (EEG), la température (T), l’électromyogramme (EMG), etc. qui sont plus fiable due à leur nature interne et non contrôler directement par l’être humain. Dans cette thèse, nous avons étudié le problème de la détection des émotions humaines chez un conducteur de véhicule en se basant sur le signal ECG. Pour cela, nous avons proposé trois contributions liées à la détection des émotions. La première est une approche d’optimisation des paramètres de classification des catégories des signaux ECG qui parmi elle une classe ECG anormale représentant un état émotionnel inhabituel. La deuxième contribution est une version améliorée de l’approche Random Forest pour la dé- tection de l’état du stress d’un conducteur. La troisième contribution est un système de détection d’émotions en suggérant une approche d’apprentissage profond ; il s’agit d’un nouveau réseau de ivneurones convolutif et d’augmentation de données qui considère la variabilité de la fréquence cardiaque (Heart Rate Variability -HRV-) comme critère essentiel de détection. Le système proposé a été bien développé et prouvé par une étude de validation et une comparaison avec les travaux de référence similaires proposés dans la littérature

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models

Digital phenotyping through multimodal, unobtrusive sensing

The growing adoption of multimodal wearable and mobile devices, such as smartphones and wrist-worn watches has generated an increase in the collection of physiological and behavioural data at scale. This digital phenotyping data enables researchers to make inferences regarding users’ physical and mental health at scale, for the first time. However, translating this data into actionable insights requires computational approaches that turn unlabelled, multimodal time-series sensor data into validated measures that can be interpreted at scale. This thesis describes the derivation of novel computational methods that leverage digital phenotyping data from wearable devices in large-scale populations to infer physical behaviours. These methods combine insights from signal processing, data mining and machine learning alongside domain knowledge in physical activity and sleep epidemiology. First, the inference of sleeping windows in free-living conditions through a heart rate sensing approach is explored. This algorithm is particularly valuable in the absence of ground truth or sleep diaries given its simplicity, adaptability and capacity for personalization. I then explore multistage sleep classification through combined movement and cardiac wearable sensing and machine learning. Further, I demonstrate that postural changes detected through wrist accelerometers can inform habitual behaviours and are valuable complements to traditional, intensity-based physical activity metrics. I then leverage the concomitant responses of heart rate to physical activity that can be captured through multimodal wearable sensors through a self-supervised training task. The resulting embeddings from this task are shown to be useful for the downstream classification of demographic factors, BMI, energy expenditure and cardiorespiratory fitness. Finally, I describe a deep learning model for the adaptive inference of cardiorespiratory fitness (VO2max) using wearable data in free living conditions. I demonstrate the robustness of the model in a large UK population and show the models’ adaptability by evaluating its performance in a subset of the population with repeated measures ~6 years after the original recordings. Together, this work increases the potential of multimodal wearable and mobile sensors for physical activity and behavioural inferences in population studies. In particular, this thesis showcases the potential of using wearable devices to make valuable physical activity, sleep and fitness inferences in large cohort studies. Given the nature of the data collected and the fact that most of this data is currently generated by commercial providers and not research institutes, laying the foundations for responsible data governance and ethical use of these technologies will be critical to building trust and enabling the development of the field of digital phenotyping.I was funded by GlaxoSmithKline and the Engineering and Physical Sciences Research Council. I was also supported by the Alan Turing Institute through their Enrichment Scheme

Extraction and Detection of Fetal Electrocardiograms from Abdominal Recordings

The non-invasive fetal ECG (NIFECG), derived from abdominal surface electrodes, offers novel diagnostic possibilities for prenatal medicine. Despite its straightforward applicability, NIFECG signals are usually corrupted by many interfering sources. Most significantly, by the maternal ECG (MECG), whose amplitude usually exceeds that of the fetal ECG (FECG) by multiple times. The presence of additional noise sources (e.g. muscular/uterine noise, electrode motion, etc.) further affects the signal-to-noise ratio (SNR) of the FECG. These interfering sources, which typically show a strong non-stationary behavior, render the FECG extraction and fetal QRS (FQRS) detection demanding signal processing tasks. In this thesis, several of the challenges regarding NIFECG signal analysis were addressed. In order to improve NIFECG extraction, the dynamic model of a Kalman filter approach was extended, thus, providing a more adequate representation of the mixture of FECG, MECG, and noise. In addition, aiming at the FECG signal quality assessment, novel metrics were proposed and evaluated. Further, these quality metrics were applied in improving FQRS detection and fetal heart rate estimation based on an innovative evolutionary algorithm and Kalman filtering signal fusion, respectively. The elaborated methods were characterized in depth using both simulated and clinical data, produced throughout this thesis. To stress-test extraction algorithms under ideal circumstances, a comprehensive benchmark protocol was created and contributed to an extensively improved NIFECG simulation toolbox. The developed toolbox and a large simulated dataset were released under an open-source license, allowing researchers to compare results in a reproducible manner. Furthermore, to validate the developed approaches under more realistic and challenging situations, a clinical trial was performed in collaboration with the University Hospital of Leipzig. Aside from serving as a test set for the developed algorithms, the clinical trial enabled an exploratory research. This enables a better understanding about the pathophysiological variables and measurement setup configurations that lead to changes in the abdominal signal's SNR. With such broad scope, this dissertation addresses many of the current aspects of NIFECG analysis and provides future suggestions to establish NIFECG in clinical settings.:Abstract Acknowledgment Contents List of Figures List of Tables List of Abbreviations List of Symbols (1)Introduction 1.1)Background and Motivation 1.2)Aim of this Work 1.3)Dissertation Outline 1.4)Collaborators and Conflicts of Interest (2)Clinical Background 2.1)Physiology 2.1.1)Changes in the maternal circulatory system 2.1.2)Intrauterine structures and feto-maternal connection 2.1.3)Fetal growth and presentation 2.1.4)Fetal circulatory system 2.1.5)Fetal autonomic nervous system 2.1.6)Fetal heart activity and underlying factors 2.2)Pathology 2.2.1)Premature rupture of membrane 2.2.2)Intrauterine growth restriction 2.2.3)Fetal anemia 2.3)Interpretation of Fetal Heart Activity 2.3.1)Summary of clinical studies on FHR/FHRV 2.3.2)Summary of studies on heart conduction 2.4)Chapter Summary (3)Technical State of the Art 3.1)Prenatal Diagnostic and Measuring Technique 3.1.1)Fetal heart monitoring 3.1.2)Related metrics 3.2)Non-Invasive Fetal ECG Acquisition 3.2.1)Overview 3.2.2)Commercial equipment 3.2.3)Electrode configurations 3.2.4)Available NIFECG databases 3.2.5)Validity and usability of the non-invasive fetal ECG 3.3)Non-Invasive Fetal ECG Extraction Methods 3.3.1)Overview on the non-invasive fetal ECG extraction methods 3.3.2)Kalman filtering basics 3.3.3)Nonlinear Kalman filtering 3.3.4)Extended Kalman filter for FECG estimation 3.4)Fetal QRS Detection 3.4.1)Merging multichannel fetal QRS detections 3.4.2)Detection performance 3.5)Fetal Heart Rate Estimation 3.5.1)Preprocessing the fetal heart rate 3.5.2)Fetal heart rate statistics 3.6)Fetal ECG Morphological Analysis 3.7)Problem Description 3.8)Chapter Summary (4)Novel Approaches for Fetal ECG Analysis 4.1)Preliminary Considerations 4.2)Fetal ECG Extraction by means of Kalman Filtering 4.2.1)Optimized Gaussian approximation 4.2.2)Time-varying covariance matrices 4.2.3)Extended Kalman filter with unknown inputs 4.2.4)Filter calibration 4.3)Accurate Fetal QRS and Heart Rate Detection 4.3.1)Multichannel evolutionary QRS correction 4.3.2)Multichannel fetal heart rate estimation using Kalman filters 4.4)Chapter Summary (5)Data Material 5.1)Simulated Data 5.1.1)The FECG Synthetic Generator (FECGSYN) 5.1.2)The FECG Synthetic Database (FECGSYNDB) 5.2)Clinical Data 5.2.1)Clinical NIFECG recording 5.2.2)Scope and limitations of this study 5.2.3)Data annotation: signal quality and fetal amplitude 5.2.4)Data annotation: fetal QRS annotation 5.3)Chapter Summary (6)Results for Data Analysis 6.1)Simulated Data 6.1.1)Fetal QRS detection 6.1.2)Morphological analysis 6.2)Own Clinical Data 6.2.1)FQRS correction using the evolutionary algorithm 6.2.2)FHR correction by means of Kalman filtering (7)Discussion and Prospective 7.1)Data Availability 7.1.1)New measurement protocol 7.2)Signal Quality 7.3)Extraction Methods 7.4)FQRS and FHR Correction Algorithms (8)Conclusion References (A)Appendix A - Signal Quality Annotation (B)Appendix B - Fetal QRS Annotation (C)Appendix C - Data Recording GU

Impact of individual differences in glucocorticoid adaptation to stress on behavior, neurophysiology and metabolism

The stress system is a key modulator of homeostasis and allows organisms to adapt to environmental changes. Proper survival is dependent on the appropriate stress response, for example initiating food (energy) intake or provoking physical reaction. However, long-term activity of the stress system is related to cardiovascular diseases, metabolic syndromes as well as accelerated aging and cognitive impairments. In order to assess the impacts of stress regulation on cardiac, metabolic and aging health processes, we used lines of rats selected for their differential glucocorticoid responsiveness to stress during juvenile period, in the different experiments presented here. The three lines of rats showed low, intermediate or high response to stress and elicited differences in biobehavioral phenotypes. Cardiovascular diseases are highly exacerbated by stress exposure and autonomic imbalance. Reduced vagal modulation has been related to a lower stress flexibility and deleterious effects on cardiac health. In a first study, we investigated the autonomic nervous system modulation of heart rate in the three lines of rats (differing in their responsiveness to stress). Electrocardiographic recordings were performed at rest and following autonomic pharmacological manipulations. Rats with intermediate reactivity to stress had a higher resting parasympathetic (vagal) modulation and a reduced heart rate compared to rats with low or high stress responses. Furthermore, pharmacological treatments showed that the sympathetic regulation of the heart was not impaired in rats with low and high responsiveness to stress. Stress can affect social interactions and, in return, social interactions can be the cause of critical stress. Furthermore, since the stress system is related to key metabolic mediators, we investigated in a second study, the general metabolism of rats from the three lines. Moreover, we paired rats from the different lines together, in mixed-line dyads, and we evaluated the differences in social interactions and the long-term effects of mixed-line pairing on metabolism. We used indirect calorimetry and mitochondrial respirometry to measure energy expenditure and mitochondrial function. We observed that the selection for differences in glucocorticoid responsiveness induced constitutive differences in energy expenditure and fuel use. Moreover, we showed that the biobehavioral phenotypes affected the social interactions between the different lines. Finally, long-term mixed-line pairing affected global and central metabolism of the rats, with rats from the low and intermediate responsive lines being more susceptible to changes. In a final experiment, we studied the interaction of two risk factors for cognitive decline, the secretion of corticosterone and aging. Indeed, dysfunctions of the stress system contribute and facilitate aging and increased glucocorticoids induce cognitive alterations. We assessed anxiety, stress responsiveness, coping-style and cognitive functions in a Morris water-maze at early-aging. Results indicated that the phenotype of the lines were stable throughout life and that learning, swimming strategies and reversal ability were different between the lines. Overall, we showed that this model is suitable to study the systems related to stress regulation. Future research may use this animal model in order to investigate further the relationship between opposite stress regulation and health

Novel methodologies and technologies for the multiscale and multimodal study of Autism Spectrum Disorders (ASDs)

The aim of this PhD thesis was the development of novel bioengineering tools and methodologies that provide a support in the study of ASDs. ASDs are very heterogeneous disturbs and their abnormalities are present both at local and global level. For this reason a multimodal and multiscale approach was followed. The analysis of microstructure was executed on single Purkinje neurons in culture and on organotypic slices extracted from cerebella of GFP wild-type mice and animal models of ASDs. A methodology for the non-invasive imaging of neurons during their growth was set up and a software called NEMO (NEuron MOrphological analysis tool) for the automatic analysis of morphology and connectivity was developed. Microstructure properties can be inferred also in vivo through the quite recent technique of Diffusion Tensor Imaging (DTI). DTI studies in ASDs are based on the hypothesis that the disorder involves aberrant brain connectivity and disruption of white matter tracts between regions implicated in social functioning. In this study DTI was used to investigate structural abnormalities in the white matter structure of young children with ASDs. Moreover the neurostructural bases of echolalia were investigated. The functionality of the brain was analyzed through Functional Magnetic Resonance Imaging (fMRI) using a novel task based on face processing of human, android and robotic faces. A case-control study was performed in order to study how the face processing network is altered in ASDs and how robots are differently processed in ASDs and control groups. Measurements characterizing physiology and behavior of ASD children were also collected using an innovative platform called FACE-T (FACE-Therapy). FACE-T consists of a specially equipped room in which the child, wearing unobtrusive devices for recording physiological and behavioral data as well as gaze information, can interact with an android (FACE, Facial Automaton for Conveying Emotions) and a therapist. The focus was on ECG, as from the analysis of power spectrum density of ECG it is possible to extract features related to the autonomic nervous system that is correlated with brain functionality. These studies give new insights in the study of ASDs exploring aspects not yet addressed. Moreover the methodologies and tools developed could help in the objective characterization of ASD subjects and in the definition of a personalized therapeutic protocol for each child