Bioinstrumentation
[GBIO2020]
[30h + 30h] 5 credits
This course is taught in the 1st semester.
Teacher(s): Michel Verleysen
Language: French
Level: Second
Aims
- to allow students to familiarize themselves with the specific requirements of biomedical instrumentation
- to introduce engineers to the main biomedical application areas, in order to allow them to apprehend each application in its context
- to provide students with the necessary keys allowing them to approach the bioinstrumentation literature
- to understand the basic concepts relating to information retrieval through data and signal analysis
- to apply these principles by means of designing simple (linear and non-linear) data analysis algorithms
- to understand and use the fundamental methods of signal processing and filtering
Main themes
The analysis of collected data represents a major element in the development of biomedical instrumentation. Information can be exploited only when formatted and processed in such a way that it leads to conclusions and decisions, for example in terms of diagnosis.
Data analysis, transformation, filtration, as well as machine learning are concepts closely linked to measurements themselves. The course will consist in approaching these themes, examples and concepts, situating them in their context, perceiving the meaning and impact of biomedical instrumentation, developing data and signal analysis skills, understanding fundamental methods of data acquisition, and providing the necessary basis allowing to learn more advanced methods.
Content and teaching methods
- specifics of measurements and instruments in clinic and biology
- electric and magnetic stimulation and recording
- use of other energy types (indications, methods and interest)
- safety notions (patient and user protection, asepsis and sterilization, device compatibility)
- application examples, especially those requiring a mathematical analysis (ECG, EEG, evoked potentials, etc..)
- descriptive methods of data analysis
- single- and multi-variable analysis
- linear and non-linear regression
- classification
- principal components analysis
- frequency analysis of signals, spectrum and sampling
Other information (prerequisite, evaluation (assessment methods), course materials recommended readings, ...)