Biosignals Processing and Analysis

Recommended Prerequisites

MSc in Informatics Engineering or equivalent. Basic concepts  about signal processing and programming languages (matlab).

Teaching Methods

Theoretical classes  (2 hours per week)
Presentation of the concepts, principles and fundamental techniques of biosignal processing. Examples of real situations to illustrate the practical interest of the techniques and its application to real cases.

Learning Outcomes

This course is designed to provide students with a comprehensive understanding of the advanced techniques and algorithms for bio-signal analysis. The course addresses both the fundamental theoretical aspects involving extracting useful information from biological signals for diagnostics and therapeutics, as well as specific applications using some relevant biosignals.
The course will contribute to the acquisition of the following competences:
Instrumental:
•    Analysis and synthesis of complex problems;
•    Mathematical Reasoning;
•    Abstraction and generalization;
Personal:
•    Critical reasoning.
Systematic:
•    Self-learning
•    Research.

Work Placement(s)

No

Syllabus

1. Biosignal analysis using domain transformation:
- Frequency transforms (review), time-frequency analysis (windowed transforms, wavelets and Wigner-Ville)
- Non-linear transforms (Hilbert-Huang, Caos and Complexity)
- Source separation
2. Biosignal analysis for cardiovascular system characterization (ECG, PPG and auscultation): physiology, autonomous nervous system, noise detection and removal, segmentation, feature extraction for clinical diagnosis and classification, heart rate variability, baroreflex, diagnosis and prognosis of significant diseases and dysfunctions.
3. Biosignal analysis for neural activity characterization (EEG): physiology, source separation, feature extraction and component recognition, EEG analysis for sleep fragmentation and epilepsy detection.

Head Lecturer(s)

Paulo Fernando Pereira de Carvalho

Assessment Methods

Assessment
Project involving synthesis, research and implementation: 50.0%
Exam: 50.0%

Bibliography

J. Enderle, S. Blanchard, J. Bronzino
Introduction to Biomedical Engineering
L.  Sornmo and P.Laguna
Bioelectrical Signal Processing in Cardiac and Neurological Applications
Several Journal and conference papers