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Tackling the Inverse Problem for Non-Autonomous Systems
This thesis presents a new method for following evolving interactions between coupled oscillatory systems of the kind that abound in nature. Examples range from the subcellular level, to ecosystems, through climate dynamics, to the movements of planets and stars. Such systems mutually interact, adjusting their internal clocks, and may correspondingly move between synchronized and non-synchronized states. The thesis describes a way of using Bayesian inference to exploit the presence of random fluctuations, thus analyzing these processes in unprecedented detail. It first develops the basic theory of interacting oscillators whose frequencies are non-constant, and then applies it to the human heart and lungs as an example. Their coupling function can be used to follow with great precision the transitions into and out of synchronization. The method described has the potential to illuminate the ageing process as well as to improve diagnostics in cardiology, anesthesiology and neuroscience, and yields insights into a wide diversity of natural processes.
Predictive Imagable Biomarkers for Brain Disorders
Recent research on Neurodegenerative disorders such as Alzheimer's Disease, Parkinson's Disease, etc. and Neuropsychiatric disorders such as Schizophrenia, has shown strong evidence that altered brain tissue structure, physiology, and connectivity reflect the extent of severity of behavioral and physical abnormalities. With the advancement of high field strength MR technologies like MRS (Magnetic Resonance Spectroscopy), fMRI (functional Magnetic Resonance Imaging) and DTI (Diffusion Tensor Imaging), it has become possible to non-invasively measure these changes brain microenvironment in terms of levels of antioxidants; neurotransmitters; regional activity, susceptibility and connectivity du...
Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications
Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging,...
Handbook of Medical Image Computing and Computer Assisted Intervention
Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. - Presents the key research challenges in medical image computing and computer-assisted intervention - Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society - Contains state-of-the-art technical approaches to key challenges - Demonstrates proven algorithms for a whole range of essential medical imaging applications - Includes source codes for use in a plug-and-play manner - Embraces future directions in the fields of medical image computing and computer-assisted intervention
Nonlinear Dynamics in Physiology
This book provides a compilation of mathematical-computational tools that are used to analyze experimental data. The techniques presented are those that have been most widely and successfully applied to the analysis of physiological systems, and address issues such as randomness, determinism, dimension, and nonlinearity. In addition to bringing together the most useful methods, sufficient mathematical background is provided to enable non-specialists to understand and apply the computational techniques. Thus, the material will be useful to life-science investigators on several levels, from physiologists to bioengineer.Initial chapters present background material on dynamic systems, statistics...
Cardiac Fibrillation-defibrillation: Clinical And Engineering Aspects
This compendium gives a comprehensive overview of the advances in fibrillation-defibrillation knowledge — recognition of fibrillation as a unique life threatening cardiac arrhythmia; discovery of the electric discharge in its double role of culprit and savior; and technological improved contributions.The book stands on the well-known philosophy of Education-Based on Problems (or EBP), that is, take fibrillation as a medical daily problem and search for that knowledge, technique or principle trying to solve it.The book is interdisciplinary, multidisciplinary and transdisciplinary. It addresses undergraduate and graduate biomedical engineering students, physicians going into cardiology, clinical engineers and clinical engineering technicians, nurses, paramedics and emergency medical personnel.
