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Scheda Riassuntiva
Anno Accademico 2018/2019
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 096281 - BIOMEDICAL SIGNAL PROCESSING AND MEDICAL IMAGES - BIOE 440-421
  • 096279 - BIOMEDICAL SIGNAL PROCESSING AND MEDICAL IMAGES [1] - BIOE 440
Docente Signorini Maria Gabriella
Cfu 5.00 Tipo insegnamento Modulo Di Corso Strutturato

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing Ind - Inf (1 liv.)(ord. 270) - MI (363) INGEGNERIA BIOMEDICA*AZZZZ096281 - BIOMEDICAL SIGNAL PROCESSING AND MEDICAL IMAGES - BIOE 440-421
Ing Ind - Inf (Mag.)(ord. 270) - BV (478) NUCLEAR ENGINEERING - INGEGNERIA NUCLEARE*AZZZZ096281 - BIOMEDICAL SIGNAL PROCESSING AND MEDICAL IMAGES - BIOE 440-421
Ing Ind - Inf (Mag.)(ord. 270) - MI (471) BIOMEDICAL ENGINEERING - INGEGNERIA BIOMEDICA*AZZZZ096281 - BIOMEDICAL SIGNAL PROCESSING AND MEDICAL IMAGES - BIOE 440-421
055147 - BIOLOGICAL SIGNAL ANALYSIS - BIOE 540
098654 - BIOMEDICAL SIGNAL PROCESSING - BIOE 440
055148 - BIOMEDICAL IMAGING - BIOE 421
Ing Ind - Inf (Mag.)(ord. 270) - MI (476) ELECTRONICS ENGINEERING - INGEGNERIA ELETTRONICA*AZZZZ096281 - BIOMEDICAL SIGNAL PROCESSING AND MEDICAL IMAGES - BIOE 440-421
Ing Ind - Inf (Mag.)(ord. 270) - MI (481) COMPUTER SCIENCE AND ENGINEERING - INGEGNERIA INFORMATICA*AZZZZ098654 - BIOMEDICAL SIGNAL PROCESSING - BIOE 440
096281 - BIOMEDICAL SIGNAL PROCESSING AND MEDICAL IMAGES - BIOE 440-421

Obiettivi dell'insegnamento

The course is intended to recall general signal processing aspects, to introduce new methods beyond those considered in the bachelor degree and examine various clinical and research application fields. Methods. From deterministic filtering to stochastic parametric analysis: mono and multi-variate AR/MA/ARMA models and non parametric as well as parametric spectral analysis. Principal component analysis. Entropy in signal processing. Applications. Automatic analysis and classification of the electrocardiographic signal (ECG). The autonomic nervous system, cardiovascular variability signals, and cardiorespiratory interactions. Fetal ECG signal. High resolution ECG and late ventricular potentials. The central nervous system: processing of the electroencephalographic signal (EEG) and of evoked (EP) and event-related potentials (ERP).


Risultati di apprendimento attesi

The learning objective is that the student takes possession of the signal processing methods in biomedical signals. At the end of the Course students should be able to work autonomously in different practical cases also different from those addressed in the lessons and/or in the laboratory practical exercises.

Dublin Descriptors

Expected learning outcomes

Knowledge and understanding

Students will learn how to:

·        To know and use signal processing methods in biomedical field

  • Understand how to analyze biomedical signals
  • Design and apply feature extraction
  • Identify relationships between analysis parameters and physiological systems behavior

Applying knowledge and understanding

Given specific project cases, students will be able to:

  • Identify the corresponding requirements and hypothesize technical solutions
  • Work autonomously in different practical cases also different from those addressed in the lessons and/or in the laboratory practical exercises
  • Apply the theory to assess the applicability of the chosen methods
  • Develop and test signal processing solutions to solve specific application problems

Making judgements

Given a relatively complex problem, students will be able to:

·        Analyze and understand the goals, assumptions and requirements associated with that problem and to model it.

·        Define the methodological solution and evaluate its applicability

  • Identify and define all experimental and applicative steps
  • Estimate the computational effort required and the resources needed for its development, identify risks and define correction actions

Communication

Students will learn to:

  • Write a project specification document
  • Write a document summarizing the project results and make it available for a general audience
  • Communicate their work in front of their colleagues during project labs

Lifelong learning skills

  • Students will learn how to develop an applicative project
  • Students choosing to focus on the research project, will learn how to organize a research activity on some specific aspects of Biomedical signal processing through the development of innovative methodological and technical solutions and experimental data analysis.

 


Argomenti trattati

Part [1] Biomedical Signal processing

Introduction to biomedical signal processing. General block diagram of biomedical signal processing operations from analog preprocessing to digital conversion of the signals. Recall of analog filters. Numerical signals, numerical filters (FIR and IIR), and their application in biomedical field.

Cardiovascular System. ECG signal: additive noise model and main superimposed noises. Most significant configurations from clinical standpoint. High resolution ECG. Ventricular late potentials: pathophysiological aspects and processing methods.

Heart Rate Variability (HRV). Study of Autonomic Nervous System (ANS) by signal processing in short- and long-term bases. Analysis of ANS branches to HRV regulation. Pathophysiological aspects: neural control mechanisms. Fetal ECG: processing methods and enhancement of useful clinical parameters. Fetal monitoring.

Arterial blood pressure signal: detection systems and main clinical parameters. Interaction models encompassing relationship between several signals related to autonomic nervous system: ECG, arterial blood pressure and respiration; open- and closed-loop models. Pathophysiological interpretations.

Neurosensorial System. EEG (electroencephalographic) signal analysis, evoked potentials (EPs) and event-related potentials (ERPs). Review on traditional processing methods with main applications in clinical and research environments: cerebral activity detected on the scalp, at cortical level and in deep brain stimulation (DBS).

Signal processing and parametric identification. Time series analysis extracted from biological signals. Examples in ECG, EEG, EP in pre-processing, filtering, prediction, extraction and estimation of parameters.

Diagnostic Classification and Modelling. Deterministic and stochastic identification. Non parametric and parametric models. FFT and DFT power spectrum analysis. Review on stochastic identification approaches. Bayes classifiers. Model families: AR/MA/ARMA (autoregressive and moving average). Models with exogenous input X. Parametric spectral analysis: backward and forward, maximum entropy methods, Pisarenko, Prony. Comparison with traditional non-parametric techniques: examples on HRV signals, EEGs and EPs. 

Classification methods: Principal Component Analysis (PCA): general introduction and applications. Independent Component Analysis (ICA).

Entropy Analysis: methods and applications. Approximate and Sample Entropy.

Laboratory activities: Exercises with Matlab implementing signal analysis tools. Application to biomedical signals. Homework exercises with evaluation. Students will develop knowledge about the most important methods and applications for biomedical signal processing and analysis working on biomedical signal samples.

Part [1] Biomedical Signal processing (short summary)

The course is intended to recall general signal processing aspects, to introduce new methods beyond those considered in the bachelor degree and cover various clinical and research application fields. Methods. From deterministic filtering to stochastic parametric analysis: mono and multi-variate AR/MA/ARMA models and non parametric as well as parametric spectral analysis. Principal component analysis. Entropy in signal processing. Applications. Automatic analysis and classification of the electrocardiographic signal (ECG). The autonomic nervous system, cardiovascular variability signals, and cardiorespiratory interactions. Fetal ECG signal. High resolution ECG and late ventricular potentials. The central nervous system: processing of the electroencephalographic signal (EEG) and of evoked (EP) and event-related potentials (ERP).


Prerequisiti

it is advisable to be aware of the fundamentals of digital signal processing.


Modalità di valutazione

Assessment

The assessment will be based on a written exam with exercises and open questions covering all the Course topics and on a project developed in the Laboratory part of the Course. The project will be presented to the instructor at the end of the course. Practical assessments given as lab activity (not mandatory) and made by students will complete the exam.

The score of the written exam, the project part and the practical assessments will be summed to compute the total score. 30 cum laude will be assigned when the total score is equal or higher than 16.

The written exam will cover all course topics both in signal and image processing. It will assign up to 12 points and will be considered sufficient when the score will be equal or higher than 7. The project part will assign up to 3 points. Two Lab assignments will assign up to 2 points. These Lab assignments are not mandatory. Students can take the written part at any exam session during the year. No mid-term assessments will be scheduled.

 

Type of assessment

Description

Dublin descriptor

Written test

·        Solution of numerical problems

·        Exercises focusing on application aspects

·        Theoretical questions on all course topics with open answer

1,2
1, 2, 3, 4, 5

1, 4, 5

Assessment of laboratory practical activity

·        Evaluation of the project

·        Assessment of the computational and experimental work developed by students either individually or in groups

2, 3, 4, 5

Oral presentation

·        Evaluation of the presentation of the project work activity developed as part of laboratory activities by students either individually or in groups

2, 3, 4, 5

 


Bibliografia
Risorsa bibliografica obbligatoriaCourse Notes https://beep.metid.polimi.it/
Risorsa bibliografica facoltativaVinay K. Ingle and John G. Proakis, Digital Signal Processing Using MATLAB, Third Edition , ISBN: 978-1-111-42737-5
Risorsa bibliografica facoltativaRangaraj M. Rangayyan, Biomedical Signal Analysis , Editore: John Wiley & Sons., Anno edizione: 2002, ISBN: 0-471-20811-6
Risorsa bibliografica facoltativaA.V. Oppenheim, R.W. Schafer, Digital signal processing, Editore: Prentice Hall, Anno edizione: 1975
Risorsa bibliografica facoltativaG. De Nicolao, R. Scattolini, Identificazione Parametrica, Editore: Cusl Milano, Anno edizione: 1997
Risorsa bibliografica facoltativaJ.G. Webster, Medical Instrumentation : Application and Design , Editore: Houghton Mifflin Co,, Anno edizione: 2010

Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
30:00
45:00
Esercitazione
10:00
15:00
Laboratorio Informatico
10:00
15:00
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
0:00
0:00
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
23/10/2019