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B019067 - BIOMEDICAL SIGNAL PROCESSING
Main information
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Further information
Type of Assessment
Course program
Academic Year 2018-19
Coorte 2018 - Second Cycle Degree in INGEGNERIA BIOMEDICA
Course year
First year - First Semester
Belonging Department
Information Engineering (DINFO)
Course Type
Single education field course
Scientific Area
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING
Credits
9
Teaching Hours
72
Teaching Term
24/09/2018 ⇒ 21/12/2018
Attendance required
No
Type of Evaluation
Final Grade
Course program
show
Lectureship
Suggested readings (Search our library's catalogue)
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
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
Learning Objectives
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.
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.
Prerequisites
math analysis, linear algebra, signal processing basics, physiology basics
Teaching Methods
The course will mainly take place in the classroom with front lessons and application seminars, using slides, notes and photocopies of didactic material, links to sites of interest
Some lessons will be replaced by visits to specialist clinics and operating rooms where students will be able to interact directly with medical staff.
Some lessons will be replaced by visits to specialist clinics and operating rooms where students will be able to interact directly with medical staff.
Further information
Seminars and visits will be announced to students beforehand and the announcement of the venue and time will be included in the communications of the School of Engineering
Type of Assessment
The learning test will consist of a part to verify the knowledge acquired by the student during the course regarding:
- Ability to analyze the clinical aspects of the signal under study
- ability to perform a state-of-the-art bibliographic research on the specific problem under study
- ability to implement Matlab functions to solve the problem
- ability to interpret the results of the analysis, including statistical techniques
- ability to interact with the clinical environment
- in general, develop a critical mindset that will allow a wide range of issues to be addressed in biomedical signal analysis
- Ability to analyze the clinical aspects of the signal under study
- ability to perform a state-of-the-art bibliographic research on the specific problem under study
- ability to implement Matlab functions to solve the problem
- ability to interpret the results of the analysis, including statistical techniques
- ability to interact with the clinical environment
- in general, develop a critical mindset that will allow a wide range of issues to be addressed in biomedical signal analysis
Course program
Elements of physiology: the human body: main apparatus and physiological systems
The human body as a dynamic system. What is a dynamic system.
Models, signals and biomedical systems
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).
Fourier discrete 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, oximetry, blood flow, ultrasonography, analysis of the human voice
Nonlinear systems
chaotic, fractal systems
applications to biomedical data analysis
Model verification and data analysis
Statistics
Seminars and meetings with Clinicians and biomedical engineers with whom scientific collaborations are active.
The human body as a dynamic system. What is a dynamic system.
Models, signals and biomedical systems
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).
Fourier discrete 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, oximetry, blood flow, ultrasonography, analysis of the human voice
Nonlinear systems
chaotic, fractal systems
applications to biomedical data analysis
Model verification and data analysis
Statistics
Seminars and meetings with Clinicians and biomedical engineers with whom scientific collaborations are active.