Ing Ind - Inf (1 liv.)(ord. 270) - MI (363) INGEGNERIA BIOMEDICA
055493 - BIOELECTRIC SIGNAL PROCESSING AND MODELING
The goal of this course is to enable students to master basic signal processing and modeling methods aimed at extracting meaningful information from biomedical signals. The course covers the fundamentals of systems analysis for modeling specific mechanisms observable from the human organism, and provides basic rationales for developing specific approaches aimed at improving medical diagnosis, treatment and rehabilitation. The course provides a selected overview of some of the most important signal processing tools. The main paradigm theme developsthrough critical conceptual frameworks related to Mathematics, Statistics and Information Theory. Modeling algorithms span from simple modeling mathematical constructs to modeling more complex frameworks inspired by physiology. 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 basic 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. (DD2)
Evaluate the pros and cons of alternative approaches. 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. (DD3)
Properly manage and apply basic methods and algorithms in order to perform statistical analysis and modeling by basic signal processing of evenly and unevenly sampled time series. (DD2)
Understand various physiological and clinical settings from the standpoint of the information extracted by the learned algorithms. (DD1)
Introduction to Modeling of Biological Systems
Signals and the Biological world. Systems Theory. Systems Identification. Statistical Modeling. Stimulus-Response models. Transfer Function Models, State-space models.
Signals as heralds of information. Information Theory
Elements of Signal Processing
Analog to digital conversion: from acquisition to digitalization, sampling, aliasing, quantization error.
Introduction on digital signals and their properties: mean, variance, autocorrelation, stationarity, ergodicity.
From time to frequency: the Fourier transform.
Non-parametric spectral analysis: power spectral density, Periodogram, Bartlett, Welch for power spectral density, cross spectrum, and coherence. Parametric spectral analysis: the system identification workflow. Investigation of different model families: AR, ARX, MA, ARMA and optimal order identification.
Principal component analysis: feature dimensionality reduction. Mathematical description of the technique and application to biological signals.
Signal processing in the context of Artificial Intelligence. Regression and Classification.
Modeling and Signal Processing of Human Physiology
Models and methods for the analysis of membrane potentials. The Hodgkin-Huxley model. Impulse propagation and conduction in fibers. Neuron models and networks. Extra-cellular potentials. Unraveling the Neural Code.
The Electrocardiographic signal. Heart rate variability: regulation on the heart rhythms by the autonomic nervous system. Time and frequency analyses. Statistical approaches to estimate heartbeat dynamics.
The Electro- and Magneto- Encephalographic signals. Introduction to the forward and inverse problem. Methods for the evaluation of electric and magnetic fields. Identification of signal rhythms in different frequency bands. Evoked potentials.
Practical examples of basic signal processing
Practical examples of simple mathematical models
Examples on the Neural Code.
Basic cardiovascular models from ECG processing
Basic methods for EEG analysis.
At the time of attendance, students are required to be familiar with basic principles, methods and algorithms of mathematics, statistics and experimental physics.
Modalità di valutazione
During the year, mandatory exercise tests are assigned on selected topics covered up to each milestone point. The evaluation of these tests contributes to the final grade up to a maximum of 5 points. The final exam can be taken in each of the scheduled sessions in January-February (highly recommended), June-July and September. The evaluation will be given on the basis of a final oral test over the topics presented in the Lectures and Exercise hours. During the exam, the students will have to:
Demonstrate the degree of knowledge and comprehension of the key aspects of the course, presenting the used methodologies in a clear and exhaustive way
Demonstrate their ability to apply the learned notions to solve exercises and real problems, on any of the topics covered in the course.
Notes for the studentsBEEPLecture SlidesBEEPTompkins W.J., Biomedical Digital Signal Processing, Editore: Anno edizione: 1993, Editore: Prentice Hall, Anno edizione: 1993
Rangayan R.M.,, Biomedical Signal Analysis. A case study approach,, Editore: Wiley Interscience, Anno edizione: 2002
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