B019067 - BIOMEDICAL SIGNAL PROCESSING

Italian

M.Luise, G.M.Vitetta, "Teoria dei segnali", McGraw-Hill, Milano, IT, 2009
G.D. Baura, "System theory and practical applications of biomedical
signals", Wiley & Sons, Inc., Pub., IEEE Press, USA, 2002
E.N.Bruce, "Biomedical signal processing and signal modelling", Wiley &
Sons, Inc., Pub., USA, 2001
R.B.Northrop, "Signals and systems analysis in biomedical engineering",
CRC 2003
A.B.Ritter, S.Reisman, B.B.Michniak, "Biomedical Engineering principles",
CRC 2005.
J.Enderle, Blanchard, Bronzino: “Introduction to Biomedical Engineering”,
Elsevier, 2005.
Wiley Enciclopedia of Biomedical Engineering, (Metin Akay Ed.), Hoboken:
John Wiley & Sons, Inc., 2006
M. Akay. "Biomedical Signal Processing", Academic Press, San Diego,
1994.
S.L.Marple, "Digital Spectral Analysis with Applications", Prentice-Hall,
Englewood Cliffs, NJ, USA, 1987

The course aims at deepening some theoretical and practical aspects of
biomedical engineering, to identify, formulate and solve, in an innovative
way, complex problems often requiring an interdisciplinary approach.
Notions regarding the processing of information of medical interest will
be provided, namely data and signals in the biomedical field, with
references on the basic concepts, the development of analysis
techniques and application examples for the acquisition, numerical
processing and classification of signals of medical and biological interest.
The students will be provided with theoretical knowledge of discrete -time
signals, stochastic processes, non-stationary methods, spectral
estimators and innovative techniques of biomedical signal analysis,
enabling them to deepen further issues by evaluating their strengths and
weaknesses.

math analysis, linear algebra, signal processing, basics of physiology

The course consists of methodological and application lessons, using slides from the lessons.
If possible, the course will be complemented by seminars offered by researchers and professors of
disciplines of biomedical interest.

If possible, some lessons will be replaced by visits to specialist clinics and operating
rooms where students will be able to interact directly with medical staff.

The exam will consist of a part aimed at
testing the knowledge acquired by the student during the course
relatively to:
- Ability to analyze the clinical aspects of the signal under study
- ability to carry out a bibliographic search on the state of the art
related to the specific problem under study
- ability to implement and use Matlab functions to solve the problem
- ability to interpret the results of the analysis, including statistical and classification techniques
- ability to interact with the clinical environment
- in general, develop a critical mind to face a
wide range of problems in the analysis of biomedical signals

The human body as a dynamic system. What is a dynamic system.
Models, signals and biomedical systems.
A/D; filters.
Data, their acquisition and characteristics, relevant time and frequency
parameters
Characteristics of signals and biomedical systems
Stochastic variables, stochastic processes and parameter estimates.
Spectral Power Density (PSD).
Discrete Fourier transform, Z transform: Theoretical basics, advantages
and limitations in biomedical applications;
stationarity, data windowing, time and frequency parameters, estimation
methods
Linear Systems Theory: Parametric Identification, Parametric Spectral
Estimation: advantages and Limits in Biomedical Applications
Non-stationary processes
Time-Frequency Analysis: Short-Time Fourier Transform, Spectrogram,
Wavelets
Filtering and noise estimation
comparison between FFT and PSD on stationary and non-stationary
biomedical signals
Examples: EEG, ECG, ultrasonography, analysis of
the human voice
Nonlinear systems
chaotic, fractal systems
applications to biomedical data analysis
Model verification and data analysis
Statistics