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Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2017/2018
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 098460 - ADVANCED SIGNALS AND DATA PROCESSING IN MEDICINE [I.C.]
  • 098459 - ADVANCED SIGNALS AND DATA PROCESSING IN MEDICINE 2
Docente Barbieri Riccardo
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 (Mag.)(ord. 270) - MI (471) BIOMEDICAL ENGINEERING - INGEGNERIA BIOMEDICA*AZZZZ098460 - ADVANCED SIGNALS AND DATA PROCESSING IN MEDICINE [I.C.]
085817 - ANALISI AVANZATE DEI DATI PER LA MEDICINA E LA BIOINFORMATICA [C.I.]

Programma dettagliato e risultati di apprendimento attesi

098459 - ADVANCED SIGNALS AND DATA PROCESSING IN MEDICINE 2 – 5 CFU – BARBIERI RICCARDO

This course provides a selected overview of some of the most successful signal processing tools used in Medicine. The main paradigm theme focuses on critical conceptual concepts related to the field of Information Theory. Algorithms span from simple modeling mathematical constructs to modeling complex frameworks inspired by statistics. The course aims at introducing advanced signal processing methods and at integrating modeling and processing methods in order to obtain relevant physiological and clinical information. Applications relevant to the Central Nervous System, to the Autonomic Nervous Systems, to the Cardiovascular System, to the Respiratory Systems and their interactions will be considered.

BRIEF INDEX:

  • Introduction to Information Theory
  • Statistical modeling: Bayes' rule, State-Space Models, Neural and Cardiovascular Point Process Models.
  • Blind source separation: Use of principal component analysis (PCA) and independent component analysis (ICA) for filtering.
  • Multivariate Causality and Higher Order Spectra.
  • Highlight on Statistical Learning and Data Mining.
  • Regression and Classification.
  • Machine Learning: Graph Search, Constraint Satisfaction, Nearest Neighbors, Decision Trees, Neural Networks, Support Vector Machines.
  • Physiological Brain Correlates: Combining Brain Imaging techniques with Physiological variables.

Expected competencies developed by Students at the end of the course. The Students will be able to understand, design, and implement advanced signals and data processing algorithms and tools in Medicine and Biology.


Note Sulla Modalità di valutazione

The evaluation will be given on the basis of two oral exams over the topics presented in the Lectures and Exercise hours of the two Parts [1][2] (Advanced Signals and Data Processing in Medicine) of the integrated Course. At each call, the Student may choose to take the exam relative to both Parts of the Integrated Course, or one of the two Parts, provided that he/she passes the remaining Part of the Course in one of the Sessions of the Academic Year of attendance.   The exam is passed if the Student gets a positive evaluation in both Parts. The final grade is the average of the two grades obtained in each of the two parts.

 

 

 

 


Bibliografia
Risorsa bibliografica obbligatoriaLecture Slides BEEP
Risorsa bibliografica facoltativaS. Cerutti, C. Marchesi, Advanced Methods of Biomedical Signal Processing, Editore: IEEE-Wiley Press, Anno edizione: 2001
Risorsa bibliografica facoltativaJ. Friedman, T. Hastie, R. Tibshirani, The Elements of Statistical Learning - Data Mining, Inference, and Prediction 2ed,, Editore: Springer, Anno edizione: 2008

Mix Forme Didattiche
Tipo Forma Didattica Ore didattiche
lezione
30.0
esercitazione
20.0
laboratorio informatico
0.0
laboratorio sperimentale
0.0
progetto
0.0
laboratorio di progetto
0.0

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
schedaincarico v. 1.6.5 / 1.6.5
Area Servizi ICT
20/10/2020