biomedical-signal-processing
| Lecturer | Jean-Marc Vesin |
| Language | French |
| Semester | Fall |
| Section | Communication Systems Computer Science Electrical and Electronics Engineering Mathematics Minors |
| Type of teaching | Ex cathedra, Matlab sessions |
| Required prior knowledge | Signal processing for telecommunications |
Objectives
Biomedical signals constitute a very interesting application field for advanced signal processing techniques, be it for pre-processing (noise reduction…) or analysis. The goal of this course is to introduce these advanced techniques and to form students to their use on experimental biomedical signals.
Content
1. Generalities on biomedical signal processing
2. Linear modeling
• linear prediction
• parametric spectral estimation
• transfer function estimation
• adaptive prediction
• model selection criteria
3. Nonlinear modeling
• polynomial models
• multi-layer perceptron
• radial basis functions
• model selection criteria
4. Time-frequency analysis
• wavelet analysis
• Wigner-Ville transform and related transforms
5. Classification
• classical classifiers
• neural network based classifiers
6. Miscellaneous (if time permits)
• higher order statistics
• principal component analysis
• source separation
Bibliography
Notes polycopiées
Links
introduction-to-biomedical-signal-processing
| Lecturer | Jean-Marc Vesin |
| Language | French |
| Semester | Spring |
| Section | Life Sciences and Technologies |
| Type of teaching | Ex cathedra, Matlab sessions |
| Required prior knowledge |
Objectives
Biomedical signals constitute a very interesting application field for advanced signal processing techniques, be it for pre-processing (noise reduction) or analysis. The goal of this course is to introduce these advanced techniques and to form students to their use on experimental biomedical signals.
Content
2. Linear modeling
• linear prediction
• parametric spectral estimation
• transfer function estimation
• model selection criteria
3. Adaptive systems
• adaptive prediction
• interference cancelling
4. Time-frequency analysis
5. Applications
• cardiovascular signals
• electroencephalogram
• transcranial Doppler signals
Bibliography
Notes polycopiées
Links
Moodle
Course book
nonlinear-signal-modeling-and-prediction
| Lecturer | Jean-Marc Vesin |
| Language | English |
| Semester | Spring |
| Section | EDIC |
| Type of teaching | Ex cathedra, Matlab sessions |
| Required prior knowledge |
Objectives
.
Content
- Basic concepts
- Minimum description length criterion
- Nonlinear polynomial predictors
- Surrogate data
- Linearity tests
- Neural networks
- Threshold AR models
- Forecast
- ARCH models
- Chaos
- Kernel approaches
Bibliography
Given during the course
Links
acquisition-et-traitement-des-donnees
| Lecturer | Jean-Marc Vesin |
| Language | French |
| Semester | Spring |
| Section | Sciences du Sport |
| Type of teaching | Ex cathedra, Matlab sessions |
| Required prior knowledge |
Objectives
Introduction to
Content
Bibliography
Notes polycopiées