Ing Ind - Inf (Mag.)(ord. 270) - MI (471) BIOMEDICAL ENGINEERING - INGEGNERIA BIOMEDICA
098460 - ADVANCED SIGNALS AND DATA PROCESSING IN MEDICINE [I.C.]
085817 - ANALISI AVANZATE DEI DATI PER LA MEDICINA E LA BIOINFORMATICA [C.I.]
The goal of this integrated course is to enable students to master advanced bioengineering methods aimed at extracting meaningful information from biomedical signals, images and data. The course covers the fundamentals of less traditional, advanced approaches aimed at improving medical diagnosis, treatment and rehabilitation. This part of the course provides a selected overview of some of the most up-to-date signal processing tools used in Medicine. The main paradigm theme focuses on critical conceptual concepts related to the field of Information Theory. Algorithms span from simple modeling mathematical constructs to modeling complex frameworks inspired by statistics. Each topic is treated both theoretically and practically, with pivotal examples relative to the neural and cardiovascular systems, focusing on the central and peripheral autonomic nervous system.
Risultati di apprendimento attesi
Students will be able to understand, design, and implement advanced signals and data processing algorithms and mathematical tools in the context of Medicine and Biology. In particular, students will learn how to: • Identify a specific problem in dealing with targeted medical information and apply the most suitable algorithm among the various ones explained in the course. • Evaluate the pros and cons of alternative approaches with respect to more established ones. Identify specific classes of common problems in biomedical information processing in a wide range of applications for different systems or organs, at various scales of investigation. • Properly manage and apply advanced methods and algorithms in order to perform statistical analysis and modeling, multivariate and nonlinear signal processing, regression and classification from evenly and unevenly sampled time series. • Understand various complex physiological and clinical settings from the standpoint of information detection and classification.
Methods. Introduction to Information Theory. Statistical modeling: Bayes' rule, State-Space Models, Point Process Models. Blind source separation: Use of principal component analysis (PCA) and independent component analysis (ICA) for filtering. Multivariate Causality and Higher Order Spectra. Highlights on Statistical Learning and Data Mining. Regression and Classification. Machine Learning: Graph Search, Constraint Satisfaction, Nearest Neighbors, Decision Trees, Neural Networks, Support Vector Machines. Unsupervised Learning.
Applications. Unraveling the Neural Code. Neural and Cardiovascular Point Process Models. EEG and MEG Blind Source Separation. Higher order analysis of cardiorespiratory signals and EEG recordings. Physiological Brain Correlates: Combining Brain Imaging techniques with Physiological variables. Classification: USA arrest data, the Heart Data example, gene expression data, breast cancer microarray study.
At the time of attendance, students are required to be familiar with the basic principles, methods and algorithms of biomedical signal and data processing. These topics are generally provided inside a course either at the Bachelor Degree or propedeutic to an advanced course on signal processing.
Modalità di valutazione
The evaluation will be given on the basis of two oral exams over the topics presented in the Lectures and Exercise hours of the two Parts  (Advanced Signals and Data Processing in Medicine) of the integrated Course. At each call, the Student may choose to take the exam relative to both Parts of the Integrated Course, or one of the two Parts, provided that he/she passes the remaining Part of the Course in one of the Sessions of the Academic Year of attendance. The exam is passed if the Student gets a positive evaluation in both Parts. The final grade is the average of the two grades obtained in each of the two parts.
Lecture Slides BEEPS. Cerutti, C. Marchesi, Advanced Methods of Biomedical Signal Processing, Editore: IEEE-Wiley Press, Anno edizione: 2001
J. Friedman, T. Hastie, R. Tibshirani, The Elements of Statistical Learning - Data Mining, Inference, and Prediction 2ed,, Editore: Springer, Anno edizione: 2008
Tipo Forma Didattica
Ore di attività svolte in aula
Ore di studio autonome
Laboratorio Di Progetto
Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese