Teaching code

4S009891

Credits

6

Coordinator

Silvia Francesca Storti

Language

Italian

Scientific Disciplinary Sector (SSD)

ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING

Courses Single

Authorized

Moodle Seminars 0

The teaching is organized as follows:

Learning objectives

The teaching goals of the course will address the applicatiopin of physics and mathematical models to biological systems

Prerequisites and basic notions

-

Program

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Modulo A - Teoria
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(1) Main biomedical signals and images. Origin, characteristics and acquisition of the main bioelectric signals (electroencephalographic signal - EEG, magnetoencephalographic – MEG, electrocardiographic - ECG, electromyographic - EMG, spontaneous and induced signals, evoked potentials - EP, event-related potentials - ERP). (2) Analysis techniques in the time and frequency domains. Fundamentals of digital signal processing and characterization in the time domain. Digital filtering methods, sampling, A/D conversion. Classic methods for frequency analysis; frequency bands and power spectrum, periodogram; time/frequency resolution; bispectra and coherence; feature extraction methods. Brain source imaging (direct and inverse problems for EEG and MEG signals) and functional and effective connectivity analysis methods. Applications on in-silico and real signals. (3) Statistical analysis of biomedical data. Review of basic concepts of descriptive and inferential statistics.
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Modulo A - Laboratorio
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The course includes a series of laboratories in the computer lab with hands-on activities mainly in MATLAB environment aimed at familiarizing students with the main analysis methods of biomedical signals and images (e.g. ECG, EMG, EEG, evoked potentials). The laboratories complement lectures by consolidating learning and developing problem-solving and hands-on practical skills in the context of bioengineering.
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Modulo B
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Introduction to the biophysics of neuronal activity. The equivalent circuit model of the cell membrane. Mathematical modeling of the membrane potential through ordinary differential equations of the single compartment model. The Hodgkin and Huxley equations for the numerical simulation of the action potential. Multi-compartmental and synaptic models. Introduction to numerical simulation of neuron and neuronal network activity. Introduction to computer technologies (Py-Neuron, Nest Simulator) that allow the simulation of neuronal circuits and extended brain regions with single cell resolution.

Bibliography

Didactic methods

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Modulo A
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Teaching methods. Regular lectures with power point presentation and blackboard, laboratory exercises and projects. The course approach is "hands on" where students will experiment the design and data analysis with the most suitable methodologies to solve real-life clinical-medical problems. Educational material will be available to students enrolled in the course on the Moodle platform. This material includes lecture presentations in PDF format and material related to laboratory activities. For further details and supplementary materials, please refer to the reference books.
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Modulo B
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Teaching is delivered through streaming lessons. The lessons will provide python programming examples. Slides will be available on the MOODLE Portal page (in compliance with copyright).

Learning assessment procedures

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Modulo A
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Assessment is conducted via oral examination preceded by a discussion on hands on activity related to neuronal activity modeling.
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Modulo B
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Assessment is conducted via oral examination preceded by a discussion on the group project assigned during the lab.

Evaluation criteria

To pass the exam, the students must show that: - they have understood the theoretical and practical concepts of the course; - they are able to use the knowledge acquired during the course to solve the assigned problems related to the processing of biomedical signals and data; - they are able to program in MATLAB environment in the context of signal and biomedical data processing; - they are able to use the knowledge acquired during the course to manage the modeling of neuronal activity.

Criteria for the composition of the final grade

The final grade will be the average of the two grades from the two modules.

Exam language

Italiano

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