First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
INFORMATION ENGINEERING
Course unit
BIOENGINEERING LABORATORY
INP5071700, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2016/17

Information on the course unit
Degree course First cycle degree in
INFORMATION ENGINEERING
IN0513, Degree course structure A.Y. 2011/12, A.Y. 2018/19
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination BIOENGINEERING LABORATORY
Department of reference Department of Information Engineering
E-Learning website https://elearning.dei.unipd.it/course/view.php?idnumber=2018-IN0513-000ZZ-2016-INP5071700-N0
Mandatory attendance No
Language of instruction Italian
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge ALESSANDRA BERTOLDO ING-INF/06

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/06 Electronic and Information Bioengineering 6.0

Course unit organization
Period Second semester
Year 3rd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Examination board
Board From To Members of the board
4 A.A. 2018/2019 01/10/2018 15/03/2020 BERTOLDO ALESSANDRA (Presidente)
TOFFOLO GIANNA MARIA (Membro Effettivo)
DALLA MAN CHIARA (Supplente)
FACCHINETTI ANDREA (Supplente)
PEDERSEN MORTEN GRAM (Supplente)
SACCOMANI MARIAPIA (Supplente)
SAWACHA ZIMI (Supplente)
SPARACINO GIOVANNI (Supplente)
3 A.A. 2017/2018 01/10/2017 15/03/2019 BERTOLDO ALESSANDRA (Presidente)
DALLA MAN CHIARA (Supplente)
FACCHINETTI ANDREA (Supplente)
PEDERSEN MORTEN GRAM (Supplente)
RUGGERI ALFREDO (Supplente)
SACCOMANI MARIAPIA (Supplente)
SPARACINO GIOVANNI (Supplente)
TOFFOLO GIANNA MARIA (Supplente)

Syllabus
Prerequisites: The course is based on the use of the knowledge of continuous time signal analysis and its study in frequency (Fourier transform, duration and band), filters (impulse response, frequency response, transfer function), interpolation, compartimental models, linear and non-linear estimators. These knowledge can be acquired by attending the course on Systems and Models and the course on Signals and Systems.
Target skills and knowledge: The student will be able to study and to practice in Matlab:
1) methodologies to develop biological models
2) methods to estimate model parameters
3) approaches to analyze time series
4) methods to assess biological results using biostatistics.

The student will acquire the following skills:
1. to learn and critically interpret the estimation of model parameters
2. To know the main mathematical models that describe the pharmacokinetic processes
3. to know and be able to use the most used filters for EEG signals
4. to know the principal areas of application of motion analysis
Examination methods: The verification of the expected knowledge and skills is carried out with an exam test divided into two parts:
1. development of two projects aimed at verifying the ability to apply the theory in biomedical contexts;
2. written exam aimed at verifying the level of knowledge acquired during the course.
The final grade is expressed as a combination of the judgments of the two projects (70%) and of the written examination (30%).
Assessment criteria: The evaluation criteria with which the verification of knowledge and expected skills will be carried out, and appropriately declined according to the articulation of the course, will be:
1. Completeness of the acquired knowledge
2. Ability to solve a problem and critically interpret the results
3. Originality and independence in the solution of the proposed projects
Course unit contents: MODELS OF BIOLOGICAL SYSTEMS (24 hours) - maximum likelihood estimation: initial estimates, the description of the measurement error, the precision of estimates. The choice of model: AIC, BIC and f-test. Sensitivity analysis by partial differential (PD, NPD). Monte Carlo simulation. Bootstrap simulation. Case studies.
BIOLOGICAL TIME SERIES (12 hours) – Algorithms for denoising, recognition and analysis of peaks, entropy measures. Case studies
BIOSTATISTICS (12 hours)- Exploratory data analysis (indices; histograms and box plots, measures of location, dispersion, shape); some elements of inferential statistics (statistical tests). Case studies
Planned learning activities and teaching methods: MODELS (Matlab): development and identification of models for the transport and distribution of drugs, for the diffusion in porous media / heart valves, for tissue and tumor growth, for the analysis of sports performance, for systems biology: regulation circuits gene and protein
TIME SERIES (Matlab): analysis of hormone series, EEG, EMG, ECG and heart rate variability signal
BIOSTATISTICS (Matlab): statistical tests to evaluate the effect of a drug from a clinical trial. Analysis of "big data": identification of differentially expressed genes
Additional notes about suggested reading: All the teaching material presented during the lectures is made available on the platform "http://elearning.dei.unipd.it".
Textbooks (and optional supplementary readings)

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Case study
  • Problem solving

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Matlab

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