Practical Signal and System Modeling for Health
6 CFU + 2 CFU assignment
July 15-18, 2019 24h total (6 lessons)
The course addressed to doctoral students with basic knowledge in mathematics, system engineering and computer science
The technological developments that took place during the last decades have made possible an interesting convergence between the disciplines of information technology and of electronic, mechanical and biomedical engineering. One of the most innovative aspects is the collaboration of professionals in the medical field with specialists in the fields of computer processing of data and signals and control, to model, analyze and control complex systems. Examples of such systems include biological systems, such as immune response, relationships with health and mental well-being, the cardiovascular system, and vision mechanisms.
The aim of this course is to present some of the most recent results and techniques concerning signal processing, modeling and analysis of biological systems.
In particular, the most appropriate methodologies for the development of low-consumption sensors for the continuous monitoring of physiological parameters, such as the electrocardiogram, will be considered. In particular, applications of the Compressive Sensing technique will be considered for the separation of fetal beats and for diagnosis in the compressed domain.
One of the most interesting parameters related to electrocardiogram analysis is the study of Heart rate variability (HRV), as it appears to be a potential indicator for autonomic function, prediction of adverse cardiovascular outcomes, psychophysiological status, and general wellness. The course will account for the most recent methods used for preprocessing, windowing, and choosing appropriate analysis parameters.
The course will also present results related to the automatic assessment of neuropsychiatric illness, via the analysis of physiological, behavioral, and psychological changes associated with several related signals, which can be passively monitored using sensors in smartphones, wearable accelerometers, Holter monitors, and multimodal sensing approaches that fuse multiple data types.
The course will also provide fundamental new insights into the emergent behavior of complex biological and embedded systems through the use of revolutionary, highly scalable and fully automated modeling and analysis techniques.Moreover, the course will present the fundamentals of systems biology, which provides a multi-disciplinary, holistic view over the functioning of biological processes seen as complex systems of dynamically interacting entities. It will also discuss system-theoretic and control-theoretic approaches tailored to get a deeper insight into the dynamic and steady-state behaviour of biological systems, and to help design artificial biomolecular systems with a desired behaviour in synthetic biology. In particular, the huge variability and uncertainty of biological systems will be taken into account to pursue a parameter-free analysis.