ELEMENTS OF SIGNAL ANALYSIS

Single discipline educational activity

Course Sheet

Course code:

CH0008

Academic Year of enrolment:

2023

Year of supply:

2023/2024

Type of Course:

Related/Supplementary

Degree:

Course of 2nd Cycle Degree in COMPUTATIONAL COGNITIVE SCIENCE

Disciplinary Sector:

Applied Physics (Cultural Heritage, Environment, Biology and Medicine)

Language:

Italian

Location:

CHIETI

Credits:

Programme Year:

Professor and Collaborators:

PIZZELLA Vittorio

Cycle:

Second semester

Hours of classroom activity:

Individual study hours:

102

Prerequisites:

Students are required to know the notions acquired in the course of mathematics for cognitive sciences.

Objectives

The course introduces the student to the analysis of data of biological interest with specific reference to signals of interest for the human body. The aim of the course is therefore to provide the student with the knowledge of the most used techniques for the analysis of data in continuous and discrete time and to make them able to apply these techniques in the applications of cognitive neuroscience.

Contents

The course will introduce the main techniques for the analysis of signals of biological interest in general and neuroscientific in particular. The methods and software to analyze these signals will be presented and discussed with practical examples.

Extended Syllabus

The course will introduce the main techniques for the analysis of signals of biological interest in general and neuroscientific in particular. The course will cover the following topics:
Introduction to biomedical signals. Electromagnetic signals: electroencephalography, magnetoencephalography, electrocardiography, electromyography, electrogastrography. Mechanical signals. Biomedical images.
Deterministic periodic and aperiodic signals in continuous-time. Energy and power of a signal.
Fourier series and its properties. Complex formalism. Fourier series for specific types of deterministic signals. Fourier transform and its properties.
Fourier transform for one-dimensional continuous-time systems. Signal processing: filters. Signal characterization: Spectral density of energy and power. Self-correlation.
Discrete-time signals. Sampling, discrete Fourier transform. Spectral density. Digital filters. FIR filters, IIR filters. Laplace transform and z-transform.
Random signals in continuous-time. Stationary processes. Ergodic processes. Discrete-time random signals.
The course will also present practical examples of analysis of biomedical signals such as electrocardiogram and electroencephalogram.

Recommended Bibliography

Students will be provided with teaching materials in the form of handouts, as well as copies of the slides discussed during the lessons. All the material will be made available on the Teams channel and on the e-learning platform of the course.
The main reference text is:
Teoria dei Segnali
Marco Luise, Giorgio Matteo Vitetta
McGraw-Hill
The details of the parts of the text that will be addressed during the course will be made available on the Teams channel and on the e-learning platform of the course.

Methods of Provision

Conventional

Teaching Methods

The module consists of 48 hours of frontal lessons on the days indicated in the didactic calendar.
The frontal teaching includes both theoretical lessons and practical examples.
Attendance is optional, recommended, and the final exam will be the same for attending and non-attending students

Evaluation methods

Verification of learning:

The verification of the students' preparation will take place with a written and oral exam on all the contents of the course.
The written exam will include two problems of data analysis of biomedical interest. The duration of the written test is two hours. The vote of the written test will be expressed in thirtieths; if the vote is less than 18/30 the written test is not considered passed and must be repeated.
The oral exam will focus on the contents of the entire program. If, after the oral exam, the final mark obtained from the average written test and oral test should be less than 18/30, the exam must be repeated in full (including written test).
The topics covered by the exam will reflect those covered during the course and present in the program elaborated in order to lead students to reflect on the links between the various topics covered. In order to pass the exam, the student must demonstrate sufficient knowledge of all the topics of the course program that will be the subject of the written test and the oral test. The student must demonstrate that he/she has well understood the concepts presented in the course. The following will also be evaluated: the ability to apply theory to concrete examples, the methodological rigor and the appropriateness of the technical language.
In detail, a vote will be assigned between:
1) 18 and 21 if he/she will demonstrate sufficient knowledge and skills in the topics of course.
2) 22 and 25 if he/she will demonstrate good knowledge and skills in all the topics of the course.
3) 26-29 if he/she will demonstrate very good knowledge and skills in all the topics of the course and a very good degree of scientific rigor.
4) 30 if he/she will demonstrate excellent knowledge and skills in all the topics of the course and a high degree of scientific rigor.
5) 30 cum laude if the student will demonstrate excellent knowledge and skills in all the topics of the course and a high degree of scientific rigor, as well as the ability to logically connect the different topics and expand beyond what has been described during the course.

Contacts/More Information

The reception time is set on Mondays from 14:30 to 16:30 at ITAB, Chieti campus. Students are suggested to confirm their appointment by email: vittorio.pizzella@unich.it

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

Aree