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Risorse bibliografiche
Risorsa bibliografica obbligatoria
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.]
Docente Barbieri Riccardo , Cerutti Sergio
Cfu 10.00 Tipo insegnamento Corso Integrato

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

ADVANCED SIGNALS AND DATA PROCESSING IN MEDICINE

 

This integrated course aims at teaching advanced signal processing methods for combining signal and data treatment with theoretical modeling, as well as for implementation of different methods and algorithms for data-mining and classification. The objective is to improve the relevant information obtainable in Medicine for both physiological and clinical purposes through the application of these advanced methodological and processing tools.

 

PART 1 (Prof. Sergio Cerutti). From deterministic to stochastic filtering. Recursive and non-recursive estimators. Optimal estimators. Optimal filters and parametric identification: Wiener filters and Kalman filters. Adaptive filters. Time-variant parametric methods. Time-frequency analysis, time-scale and wavelet analysis. Linear approach and quadratic distributions. Complexity of biomedical systems and signals: basic definitions of systems with non-linear dynamics. Higher order analysis of biomedical signals: bispectrum and bicoherence. A new approach for the processing of biomedical information: "multi"-paradigm. Integration of the information in multivariate, multiorgan, multimodal and multiscale approaches: fusion of the information across the single scales.

 

PART 2 (Prof. Riccardo Barbieri). Introduction to Information Theory. Statistical modeling: Bayes' rule, State-Space Models, Neural and Cardiovascular Point Process Models. Blind source separation: 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.

 

The course will consider applications relevant to the Central Nervous System, to the Autonomic Nervous Systems, to the Cardiovascular System, to the Respiratory Systems and their interactions.


Note Sulla Modalità di valutazione

The evaluation will be given on the basis of an oral exam, covering the topics dealt with during the Lessons and Exercise hours of the two Parts [1][2] (Advanced Signals and Data Processing in Medicine) which constitute integrant parts of the Course. The Student may choose to take the exam relative to only one Part of the Integrated Course, provided that he/she passes the other Part of the Course within 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 single grades obtained from the two parts.


Bibliografia
Risorsa bibliografica obbligatoriaNotes for the Students Beep
Risorsa bibliografica obbligatoriaS. Cerutti, C. Marchesi eds.,, Advanced Methods of Biomedical Signal Processing, Editore: IEEE-Wiley Press, Anno edizione: 2001
Risorsa bibliografica obbligatoriaS. M. Bozic, Digital and Kalman Filtering, Editore: E. Arnold Publ, Anno edizione: 1979
Risorsa bibliografica facoltativaR.M. Rangayyan, Biomedical Signal Analysis, Editore: IEEE Press, Anno edizione: 2002
Risorsa bibliografica facoltativaJ.G. Webster, Medical Instrumentation : Application and Design, Editore: Houghton Mifflin Co., Anno edizione: 2010
Risorsa bibliografica facoltativaR.O.Duda, P.E.Hart and D.G.Stork, Pattern Classification, Editore: Wiley
Risorsa bibliografica facoltativaS.V.Vaseghi, Advanced Digital Signal Processing and Noise Reduction, Editore: John Wiley and Sons, Anno edizione: 2006
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
60.0
esercitazione
40.0
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
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.5 / 1.6.5
Area Servizi ICT
20/10/2020