• 098458 - ADVANCED SIGNALS AND DATA PROCESSING IN MEDICINE 1

Goals

The goal of the course is to enable students to master the bioengineering methods and applications that are necessary to properly develop advanced methods of enhancing information from biomedical signals, images and data. The course covers the fundamentals of non traditional and advanced approaches to improve medical diagnosis, treatment and rehabilitation. Each topic is treated both theoretically and practically, with pivotal examples relative to cardiovascular system, to respiratory system and to central and autonomic nervous systems. Through the course, a greater interaction with the class will be achieved through a kind of flipped relationship in which students will be invited to deliver questions to the class and to provide the relevant evaluation. The aim is to reach a good level of autonomy in learning and evaluating the relevance of new physiological and clinical topics.

Students will learn how to:

• Approach a determined problem in processing medical information and to apply the most suitable algorithm among the various ones reported in the course and recalled into literature
• Evaluate the pros and cons of alternative approaches in respect to the previous ones.
• Identify certain classes of common problems in biomedical information processing in the  various applications for different systems or organs, at various scales of investigation. Be skilled enough to properly manage and apply advanced methods and algorithms like time-frequency and time-scale distributions, deterministic chaotic time series, higher order analysis, etc.
• Understand various complex physiological and clinical settings from the standpoint of information detection and classification

Methods. From deterministic to stochastic filtering. Recursive and non-recursive estimators. Optimal estimators. Optimal filters and parametric identification: Wiener filters and Kalman filters. Adaptive filters. Time-variant parametric methods. Time-frequency analysis, time-scale and wavelet analysis. Linear approach and quadratic distributions. Complexity of biomedical systems and signals: basic definitions of systems with non-linear dynamics. Higher order analysis of biomedical signals: bispectrum and bicoherence. A new approach for the processing of biomedical information: "multi"-paradigm. Integration of the information in multivariate, multiorgan, multimodal and multiscale approaches: fusion of the information across the single scales.

Applications. Relative mainly to the studying of Central Nervous System, of the Autonomous Nervous System, of the Cardiovascular System, of the Respiratory system and their interactions.

Students are required to know the basic principles, methods and algorithms of biomedical signal and data processing which are generally provided inside a course at the Bachelor Degree or which is propedeutic to an advanced course on that topic.

The evaluation will be given on the basis of an oral exam, dealing with the topics dealt with in the Lessons and Exercise hours of the two Parts [1][2] (Advanced Signals and Data Processing in Medicine) which constitute integrant parts of the Course. The Student may choose to take the exam relative to only one Part of the Integrated Course, provided that he/she obligates to make the other Part of the Course within the Sessions of the Academic Year of attendance.   The exam is passed if the Student gets a positive evaluation in both the Parts. The final grade is the average of the single grades obtained in the two parts.