The Course is divided into two parts: the first one  is dedicated to Biomedical Signal Analysis. The objective is to introduce the most diffused methods of information processing from biomedical signals and to describe the most significant applications in Medicine and Biology, in both physiological studies and in clinical applications for diagnosis, therapy and rehabilitation.
The second part of the Course  deals with Medical Images. The objective is to provide basic concepts for their characterization, to illustrate clinical and diagnostic problems and to introduce the principles on which some techniques for their processing and reconstruction are based.
The Course is constituted by frontal lessons, exercise sessions, seminars and laboratory exercise aiming at more deeply analyzing the topics of signal and image processing making use of MatLab. The attendance of the Course is strongly adviced.
Part  Biomedical Signal Processing. Prof. Sergio Cerutti
Programme of lessons and exercises
Introduction to biomedical signal processing. General block diagram of biomedical signal processing operations. Recalls from analogue and digital filterings.
Cardiovascular System. ECG signal: main superimposed noises and most significant configurations from clinical standpoint. High resolution ECG. Ventricular late potentials: pathophysiological aspects and processing methods: Simson parameters. Study of Autonomic Nervous System (ANS) by means of processing of heart rate variability signals on short- and long-term bases. Pathophysiological aspects: studying of the neural control mechanisms (in particular of heart rate and arterial blood pressure). Fetal ECG: processing methods and enhancement of parameters useful from clinical standpoint. Fetal states monitoring. Arterial blood pressure signal: detection systems and main clinical parameters. Interaction models among signals related to autonomic nervous system: ECG, arterial blood pressure and respiration; open- and closed-loop models. Pathophysiological interpretations.
Diagnostic Classification. Classification methods. Principal Component Analysis (PCA): various applications.
Neurosensorial System. EEG (electroencephalographic) signal analysis, evoked potentials (EPs) and event-related potentials (ERPs). Review on traditional processing methods with main applications in clinical and in research environments: cerebral activity detected on the scalp, at cortical level and in deep brain stimulation (DBS).
Signal processing and parametric identification. Time series analysis detected from biological signals. Examples relative to ECG, EEG, EP in the various phases of pre-processing, filtering, prediction, extraction and estimation of parameters, diagnostic classification. Deterministic and stochastic identification. Review on stochastic identification approaches. Model families, in particular AR/MA/ARMA (autoregressive and moving average) and models with exogenous input X. Parametric spectral analysis, including backward and forward methods, maximum entropy methods, Pisarenko, Prony, etc. Comparison with the traditional non-parametric techniques: various examples on heart rate variability signals, EEGs and EPs.
Prerequisites: Mandatory prerequisites are not requested; it is advisable to be aware of the fundamentals of digital signal processing.
Part  Biomedical Signal processing (short summary)
The course is intended to recall general signal processing aspects, to introduce new methods beyond those considered in the bachelor degree and examine various clinical and research application fields. Methods. From deterministic filtering to stochastic parametric analysis: mono and multi-variate AR/MA/ARMA models and non parametric as well as parametric spectral analysis. Principal component analysis. Entropy in signal processing. Applications. Automatic analysis and classification of the electrocardiographic signal (ECG). The autonomic nervous system, cardiovascular variability signals, and cardiorespiratory interactions. Foetal ECG signal. High resolution ECG and late ventricular potentials. The central nervous system: processing of the electroencephalographic signal (EEG) and of evoked (EP) and event-related potentials (ERP).