Physiological Signal Processing

Code:

EBE0052

Acronym:

PSFI

Keywords
Classification	Keyword
OFICIAL	Biomedical Engineering

Instance: 2019/2020 - 1S

Active?	Yes
Web Page:	https://moodle.up.pt/course/view.php?id=2187
Responsible unit:	Department of Electrical and Computer Engineering
Course/CS Responsible:	Master in Bioengineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIB	34	Syllabus	3	-	6	70	162

Teaching language

Portuguese and english

Objectives

The objective of this course unit is to motivate students to the nature and diversity of physiological signals (e.g. 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. 

Learning outcomes and competences

Upon successful conclusion of this curricular 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.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Pre-requisites: basic knowledge in signal theory, notably discrete-time signals and systems and Fourier analysis.

Program

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-time 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. The Z transform
Definition, region of convergence, 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-time 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 a uniform bank of filters
Spectral estimation using the DFT (periodogram, spectrogram)

9. Physiological signal processing
Examples: cochlear implants, pacemakers and brain computer interfaces 

 

Mandatory literature

Oppenheim, Alan V.; Discrete-Time Signal Processing. ISBN: 0-13-216771-9

Complementary Bibliography

Bronzino, Joseph Daniel, 1937- 340; The biomedical engineering handbook
Enderle, Joseph Bronzino John; Introduction to Biomedical Engineering. ISBN: 0-12-238662-0
Bruce, Eugene N.; Biomedical signal processing and signal modeling. ISBN: 0-471-34540-7

Teaching methods and learning activities

The teaching methodology is based on lectures 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. In these classes muitiple choice questions will also be presented aiming at assessing the minimum learning objectives.

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 study-case project will also be developed during the last part of the semester.

Software

Matlab 6 R12.1

keywords

Technological sciences > Technology > Computer technology > Signal processing

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	65,00
Participação presencial	7,00
Teste	8,75
Trabalho de campo	8,75
Trabalho laboratorial	10,50
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	70,00
Frequência das aulas	70,00
Trabalho de campo	16,00
Trabalho laboratorial	6,00
Total:	162,00

Eligibility for exams

In order to be admitted to the final exam, 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 "study case" project planned for the semester. The continuous assessment combines the grades of mini-tests, the graded lab work and the study-case project. The grades of the (individual) mini-tests are weighted at 25%, those of the graded lab work (groups of two students) are weighted at 25%, and the study case project (typically groups of three students) will be weighted at 25%. In addition, in certain TP classes several verification questions will be proposed that are colectively weigthed at 25%. A minimum grade of 7/20 is mandatory for admission to the final exam.

Calculation formula of final grade

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 for either one of the two components. The attendance and participation in class may influence a residual adjustment in the final grade.

Special assessment (TE, DA, ...)

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 implying the utilization of Matlab.