Code: | EBE0052 | Acronym: | PSFI |
Keywords | |
---|---|
Classification | Keyword |
OFICIAL | Biomedical Engineering |
Active? | Yes |
Responsible unit: | Department of Electrical and Computer Engineering |
Course/CS Responsible: | Master in Bioengineering |
Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
---|---|---|---|---|---|---|---|
MIB | 26 | Syllabus | 3 | - | 6 | 70 | 162 |
MIEEC | 7 | Syllabus (Transition) since 2010/2011 | 4 | - | 6 | 70 | 162 |
5 | |||||||
Syllabus | 4 | - | 6 | 70 | 162 | ||
5 |
The objective of this course unit is to motivate students to the nature and diversity of physiological signals (for example: EMG, EEG, ECG), and to familiarize students with the theory foundations in the area of digital signal processing as well as their valorization as practical skills allowing students to understand and design important processes in physiological signal processing including acquisition, conditioning, filtering, analysis and representation of relevant information.
After successful conclusion of this course unit, students will be able to use techniques and technologies of physiological signal processing, by strengthening not only their application to diagnosis objectives, therapy and rehabilitation, but also to foster research, specialization and innovation in these areas.
1. Introduction to electrophysiology
Concept of electrophysiology
Action potentials, propagation, extracellular signals
Principles of electrocardiography (ECG), electromyography (EMG) and electroencephalography (EEG)
2. Signals and systems
Characterization and representation of discrete signals and systems
3. Sampling and reconstruction of signals
Periodic sampling of continuous signals
Frequency analysis of periodic sampling
Reconstruction of continuous signals from samples
Discrete processing of continuous signals
4. Z transform
Definition, convergence region, properties, inverse Z transform
5. Fourier analysis
The four faces of Fourier analysis
Review of the discrete-time Fourier transform
Discrete Fourier Transform (DFT) as a sampling of the discrete-time Fourier transform
Properties of the DFT
Fast calculation of the DFT using the Fast Fourier Transform (FFT)
6. Discrete filters
Types of filters
Transfer function, zero-pole representation and frequency response
Group delay and inverse system
Minimum-phase and maximum-phase filters
Linear-phase filters: types I, II, III, and IV
Design of IIR and FIR filters
structures for the realization of IIR and FIR filters
7. The auto-correlation and cross-correlation
As special cases of the discrete convolution
The Wiener-Khintchine theorem
Fast correlation using the FFT
8. Principles of spectral estimation
The DFT as an uniform bank of filters
Spectral estimation using DFT (periodogram, spectrogram)
9. Physiological signal processing
Examples: cochlear implants, pacemakers and brain computer interfaces
The teaching methodology is based on theoretical-practical classes and laboratory classes. The former include the presentation and illustration of theoretical contents of the course using multimedia support, as well as the discussion of problems and specific cases of application. The laboratory classes involve conventional or Matlab-based solving of problems that are proposed to consolidate and reinforce the applied perspective of the main topics of the course, as well as experimental work using Matlab and the Biopac platform for the acquisition and analysis of physiological signals. Additionally, mini-tests will also be solved in laboratory classes throughout the semester, and a mini-project will also be developed by groups of typically four students, during the latter part of the semester.
Designation | Weight (%) |
---|---|
Exame | 65,00 |
Teste | 10,50 |
Trabalho de campo | 14,00 |
Trabalho laboratorial | 10,50 |
Total: | 100,00 |
Designation | Time (hours) |
---|---|
Estudo autónomo | 70,00 |
Frequência das aulas | 70,00 |
Trabalho de campo | 16,00 |
Trabalho laboratorial | 6,00 |
Total: | 162,00 |
In order to be admitted to the final exams, students should comply with the General Evaluation Rules of FEUP concerning the maximum number of missed classes, and should perform the individual mini-tests, the graded lab work and the small project planned for the semester. The continuous assessment combines the grades of mini-tests, the graded lab work and the small project. The grades of the (individual) mini-tests will be weighted at 30%, those of the graded lab work (groups of two students) will be weighted at 30%, and the mini-project (typically groups of four students) will be weighted at 40%. A minimum grade of 7/20 is mandatory for admission to the final exam.
The final exam consists of a closed-book written examination whose duration is 2 hours. Students will be provided with a formulae sheet.
The final grade (FG) is obtained using the following formula which combines the grades of two components: continuous assessment (CA) and final exam (FE):
FG= 0.35 CA + 0.65 FE
The scale for both components is [0, 20]. A minimum classification of 7/20 is required either one of the two components. The attendance and participation in class may influence a residual adjustment in the final grade.
According to Number 6 of Article 5 of the General Evaluation Rules of FEUP, students who did not obtain Continuous Assessment during the semester, will have to perform an extra practical exam including the utilization of Matlab.