FUNDAMENTALS OF DATA PROCESSING AND BIOMEDICAL SIGNALS

FUNDAMENTALS OF DATA PROCESSING AND BIOMEDICAL SIGNALS

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iten
Code
80220
ACADEMIC YEAR
2021/2022
CREDITS
9 credits during the 3nd year of 8713 Biomedical Engineering (L-8) GENOVA
SCIENTIFIC DISCIPLINARY SECTOR
ING-INF/06
LANGUAGE
Italian
TEACHING LOCATION
GENOVA (Biomedical Engineering)
semester
1° Semester
Teaching materials

OVERVIEW

Signal processing applications span an immense set of discipines that include communications, space explorations, and medicine just to name a few. In particular, digital signal processing deals with the representation, transformation, and manipulation of signals and the information therein.

AIMS AND CONTENT

LEARNING OUTCOMES

 

This course aims to provide the basic knowledge on the basic methodologies for signal manipulation, digital signal processing. Particular attention will be given to biomedical applications of each presented techinque. This to provide to the student a view of direct usage of theoretical knowledge 

AIMS AND LEARNING OUTCOMES

Aims 1. Understanding the theoretical basis of the discrete signal processing. Learning outcomes for Aim 1. The students will be able to critically discuss the theoretical basis and fundamental elements of discrete signal processing (e.g. Discrete Fourier Transform, Filter design, power spectral analyses)

Aim 2. Apply signal processing techniques in clinical and scientific context of classical biomedical applications. Learning outcomes for Aim 2. The students will implement and apply the classical and more advanced methods for biomedical signal processing. They will also learn the physiological basis for classical biological signals and how these signals can be analysed to extract information relevant for diagnosis in specific pathologies.

Aim 3. Understand and appropriately apply an hypothesis test Learning outcomes for Aim 3. The students will be able to critically discuss the theoretical basis of hypothesis tests and they will be able to design and conduct an hypothesis test 

Aim 4. Problem solving in real case examples of signal analysis. Learning outcomes for Aim 4. During the working groups, the students will acquire the capability to solve specific problems of data analysis by applying the techniques acquired during the course. The working groups will be organized such that different students will assume different roles as in real lab teams.

PREREQUISITES

It is required for the student to have basic understanding of the fundamental elements of system engineering (e.g., Laplace Fourier transforms) and basic descriptive statistics - e.g., hisotgrams, mean and variance. It is strongly encouraged for the students to take a progamming course and specifically in Matlab. However, we will provide additional materials and autoevaluation tools for testing the above mentioned prerequistes

Teaching methods

 

Frontal lessons with Matlab exercises that students will solve autonomously and deliver through aulaweb-platform for their evaluation during the semestrer.

SYLLABUS/CONTENT

Digital signal processing

  • Discrete time signals : numerical representation of discerte time signals. Discerte time-fourier transforms. Sampling of continuous-time signals,
  • Discerte time systems: linear constant-coefficient difference equations. Numerical convolution. Z-transform. FIR and IIR systems. Digital filter design. Structures of discrete-time systems. 

Data processing

  • Descriptive statistics.
  • Probability and probability estimation
  • Hypothesis testing:
    • t-Student; Analisys of Variance; Type-I and Type-II errors; confidence intervals; Linear regression.

RECOMMENDED READING/BIBLIOGRAPHY

 

  • Oppenheim A.V.,  Schafer R.W. Discrete-time signal processing. 3rd Edition. Pearson New International Edition.
  • G. Filatrella, P. Romano Elaborazione statistica dei dati sperimentali, EDISES.
  • T. Vargiolu, Elementi di probabilità e statistica, CLEUP.

TEACHERS AND EXAM BOARD

Ricevimento: On appointment    

Ricevimento: With appointment: Tel. 0103532220 or marco.fato@unige.it

LESSONS

Teaching methods

 

Frontal lessons with Matlab exercises that students will solve autonomously and deliver through aulaweb-platform for their evaluation during the semestrer.

ORARI

L'orario di tutti gli insegnamenti è consultabile su EasyAcademy.

EXAMS

Exam description

The final grade will be composed of the evaluations of each group of work, a written and an oral exam.Students will self-organise in small groups (max 3) and these groups will participate in several activities during the semester. Each assignment will be evaluated for its completeness and overall quality. 

 

Assessment methods

Aim 1 and 3 will mainly be evaluated during the written and oral exam. 

Aim 2 and 4, will be evaluated during the group assignments and during the oral examination.