Object of our research is the pathophysiology of cardiovascular, respiratory, central and peripheral nervous systems. Research goals are achieved through computational models and their integration with multi-source data. New algorithms and innovative methodologies of data analysis and biomedical images are specifically tailored to each physiological system. Multivariate, multi-organ and multiscale approaches enable data information to be included in the models and then used for risk stratification and support for clinical decisions through machine learning approaches. The study and design of descriptive and predictive analysis algorithms applies in particular to big data obtained from sets of biomedical signals generated by non-contact, minimally invasive portable systems and technologies currently capable of gathering information on the well-being and health of the subject. The integration of signal and image data analysis allows the development of devices for remote monitoring, personalized diagnosis and patient empowerment. Ongoing studies include: atrial fibrillation, sleep classification, wearable cardiovascular monitoring, adult and pediatric EEG studies, fetal and infant monitoring, personalized therapies for shock patients, diagnostic e-health solutions and m-health. Multiscale data analysis involves genetic, genomic, and multi-omic analysis for the characterization of molecular mechanisms underlying pathologies and the identification of custom pharmacological targets (pharmacogenomics). A wide range of tools are devised to support the diagnostic process and the clinical decision in various cardiovascular and neurosensory pathologies in the adult, in the fetus and in the infant as well as to identify treatment solutions.