Biomedical Signal Processing
A.Y. 2024/2025
Learning objectives
The course has the goal of providing the theoretical and practical knowledge required for processing biomedical signals and time series.
Expected learning outcomes
The student will be able to extract features from biomedical signals and time-series, also by means of spectral analysis; second, she/he will be familiar with the characteristics of the main biomedical signals; finally the student will be able to design and employ digital filters to remove noise and artifacts from signals acquired by medical applications.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
First part
· Main biological signals and their properties
· Sampling of continuous time signals
· Linear Time Invariant (LTI) systems, impulse response, frequency response and transfer function
· Finite and Infinite Impulse Response filters (FIR & IIR)
· Design of IIR filters by direct placement of poles and zeros. Classic IIR filters
· Design of linear-phase FIR filters with the window method
Second part
Statistical characterization of signals
· Introduction to stochastic processes
· Autoregressive (AR) stochastic processes and their usage as model of biological signals
· Estimation theory basics
Spectral analysis
· Non parametric and parametric spectral estimates
· Spectral analysis of the heart rate variability (HRV) signal
Source separation
· Enhancement of repetitive patterns through averaging
· Cross-correlation& matched filters
Entropies and regularity of a signal
· Entropy rate of a signal and its use in the quantification of the regularity of a signal
· Entropy rate estimators for biological signals
Long time correlations and fractals signals
· Fractal sets and fractal dimension estimate
· Long memory and self-similar processes; 1/f noise
· Estimation of the scaling exponent in biological time series
· Main biological signals and their properties
· Sampling of continuous time signals
· Linear Time Invariant (LTI) systems, impulse response, frequency response and transfer function
· Finite and Infinite Impulse Response filters (FIR & IIR)
· Design of IIR filters by direct placement of poles and zeros. Classic IIR filters
· Design of linear-phase FIR filters with the window method
Second part
Statistical characterization of signals
· Introduction to stochastic processes
· Autoregressive (AR) stochastic processes and their usage as model of biological signals
· Estimation theory basics
Spectral analysis
· Non parametric and parametric spectral estimates
· Spectral analysis of the heart rate variability (HRV) signal
Source separation
· Enhancement of repetitive patterns through averaging
· Cross-correlation& matched filters
Entropies and regularity of a signal
· Entropy rate of a signal and its use in the quantification of the regularity of a signal
· Entropy rate estimators for biological signals
Long time correlations and fractals signals
· Fractal sets and fractal dimension estimate
· Long memory and self-similar processes; 1/f noise
· Estimation of the scaling exponent in biological time series
Prerequisites for admission
The class has no specific preliminary requirement, except those necessary to enter the master degree in computer science.
Teaching methods
The course will be composed of: a) academic lectures; b) solution of exercises, previously assigned to the students; c) possible implementation in Matlab of the algorithms discussed; d) scientific paper discussions.
Teaching Resources
After each class, the learning materials (either slides of the copy of what written on the electronic board) will be made available on the Ariel course web site on the new MyAriel (https://myariel.unimi.it) platform.
For the first part of the class, the reference textbook is:
James H. McClellan, Ronald W. Schafer, Mark A. Yoder
Digital Signal Processing First, Second edition (o DSP First, 2nd edition)
Pearson Education, 2016. ISBN-13: 978-1292113869
For the second part, there is yet no single text covering the breath and depth of the issues in this course and the materials will be provided on Ariel.
For the first part of the class, the reference textbook is:
James H. McClellan, Ronald W. Schafer, Mark A. Yoder
Digital Signal Processing First, Second edition (o DSP First, 2nd edition)
Pearson Education, 2016. ISBN-13: 978-1292113869
For the second part, there is yet no single text covering the breath and depth of the issues in this course and the materials will be provided on Ariel.
Assessment methods and Criteria
The oral examination will consist in a public discussion of a project: a student, or a party of two students (each contributing independently to a part of it) selects one of the scientific papers assigned in class and implements the algorithm proposed therein (in Matlab). The code is then tested on biological signals freely available on line, or provided by the instructor, or collected with a device. The student will submit (a few days before the day of the exam) the code, with clear explanations on how to run it, and then, during the exam, she/he will present in a 10 minutes (sharp) presentations (20 minutes for parties of two students, 10 minutes each student) the work.
At the end of the exam, the final grades are on a scale of 30 and they are assigned considering the following criteria: knowledge of the topics, ability in applying the knowledge acquired on practical problems, ability to resolve problems independently, clarity in expressing concepts.
At the end of the exam, the final grades are on a scale of 30 and they are assigned considering the following criteria: knowledge of the topics, ability in applying the knowledge acquired on practical problems, ability to resolve problems independently, clarity in expressing concepts.
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING - University credits: 6
Lessons: 48 hours
Professor:
Sassi Roberto
Professor(s)
Reception:
By appointment (email or phone)
Dipartimento di Informatica, via Celoria 18, stanza 6004 (6 piano, ala Ovest), Milano or remotely via Microsoft Teams