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Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2018/2019
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 052382 - BIOMEDICAL SIGNAL PROCESSING LABORATORY
Docente Mainardi Luca
Cfu 5.00 Tipo insegnamento Monodisciplinare
Didattica innovativa L'insegnamento prevede  1.0  CFU erogati con Didattica Innovativa come segue:
  • Blended Learning & Flipped Classroom

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing Ind - Inf (Mag.)(ord. 270) - MI (471) BIOMEDICAL ENGINEERING - INGEGNERIA BIOMEDICA*AZZZZ052382 - BIOMEDICAL SIGNAL PROCESSING LABORATORY

Obiettivi dell'insegnamento

The course exposes participants to design and implementation of biomedical signal processing methods in Matlab. The objective is to provide tools and programming skills through the realization of basic signal processing algorithms, working in teams under the supervision of the lecturer. Advanced topics of biomedical signal processing will be exploited through specific project assignment. Project topics range from multi-variate parametric estimate, optimal filters, time-frequency transform, to non-linear analysis, synchronization and coupling. Innovative didactic schemes will include: learning-by-doing, bleanded learning and flipped classroom.

It should be noted that this is a limited number access course. The mandatory procedure for access request is here (http://www.ccsbio.polimi.it/)

 


Risultati di apprendimento attesi

The expectations of achievements and abilities are described in the following table

 

Dublin Descriptors

Expected learning outcomes

Knowledge and understanding

* Understand the basics of biomedical signal processing in Matlab

* Knowledge of digital filter requirements for biomedical applications

* Knowledge of algorithms for waveform detection in Biosignals.

* Knowledge of spectral estimation methods.

* Understanding of noise redaction methods for biomedical signal enhancements

Applying knowledge and understanding

* Design and implement biomedical signal algorithms and tools

* Tune algorithms and methods to optimize their performance.

* Implement algorithm and tools in Matlab

Making judgements

* Evaluate the correctness of the implemented algorithms.

* Select the best options and implementation schemes.

* Compare and evaluate the performance of different solutions.

Communication

* To be able to describe signal processing methods, its advantages and performances.

* To be able to defend the implementation of a certain algorithm in front of an audience and the lecturer.

Lifelong learning skills

* To be able to understand and implement any biomedical signal processing algorithm published in literature.


Argomenti trattati

 

Introduction to software tools for scientific computing and their use for the design of biosignal processing algorithms.

Data Analysis: Data fitting, criteria for model selection and identification, LMS estimation. Interpolation.

Signal analysis: FFT and harmonic analysis of biosignals. Design of FIR and IIR filters for biomedical applications, detection of waves and patterns. Spectral estimate based on parametric and non-parametric methods, application to EEG and Heart Rate Variability signals. Enhancement of evoked potentials by averaging and single sweep approach.

It should be noted that this is a limited number access course. The mandatory procedure for access request is here (http://www.ccsbio.polimi.it/)

 


Prerequisiti

None


Modalità di valutazione

During the course students will work in small teams under the supervision of the lecturer. The teams will be asked to implement biomedical signal processing algorithms (DdD2) and discuss with the Lectruer impementation options (DdD3,DdD4,DdD5)

A final project will be assigned to each team. The project, its implementation and the related results will be discussed (DdD4) by the team during as oral exam session. The Oral session will assess the team in terms of: i) level of understanding of the problem and problem solving skills (DdD2); ii) Algorithm implementation and related options (DdD2, DdD3); iii) capability to discuss/defend the selected implementation options (DdD4); iv); overal quality of the work and algorithm performances (DdD5). 

Final score will be based on laboratory works (10%), Project delevolpment (70%) and Oral session (20%).


Bibliografia
Risorsa bibliografica facoltativaRichard Shiavi, Introduction to Applied Statistical Signal Analysis, Editore: Academic Press, Anno edizione: 2006, ISBN: 978-0120885817
Risorsa bibliografica facoltativaVirginia Stonick & Kevin Bradley, Labs for Signals and Systems Using MATLAB, Editore: CL-Engineering, Anno edizione: 1995, ISBN: 978-0534938086

Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
7:30
11:15
Esercitazione
0:00
0:00
Laboratorio Informatico
32:30
48:45
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
10:00
15:00
Totale 50:00 75:00

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.1 / 1.6.1
Area Servizi ICT
20/11/2019