|
|
|||||||||||||||||
Course Catalog 2013-2014
SGN-52606 Processing of Biosignals, 5 cr |
Additional information
The course is updated from BME-2626 Processing of physiological signals. This course implementation will include basic processing methods for systems biology data. Basics of signal processing is a pre-requisite for this course.
Person responsible
Ilkka Korhonen, Andre Sanches Ribeiro, Olli Yli-Harja, Mikko Koivuluoma
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
|
|
|
|
|
|
|
|
|
Requirements
Accepted computer assignments and final exam.
Completion parts must belong to the same implementation
Learning Outcomes
This course provides the basics of applying signal processing methods on biosignals of physiological or biological origin. Student is assumed to have basic signal processing method knowledge and basic skills for using Matlab prior the course. After this course, the student can: - describe the common properties of biosignals, and describe basic challenges in processing and analyzing them. - explain the principles of filtering and spectral analysis and select suitable methods for applications in health and biology. - analyze common methods of statistical modeling of biological data and explain the assumptions of the models. - assess the performance of a developed biosignal processing or analysis method. - apply signal processing methods to biological signals including EEG, ECG, gene expression. - implement such methods to process biological signals.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Types and origins of physiological and biological signals, and their basic properties. Basics of data acquisition, sampling, and filtering related to biosignals. | Artefacts and missing data in biosignals. | Insights in physiological and biological signal generators. |
| 2. | Filtering of biosignals. Linear filtering, filter design for biosignals. | Non-linear filtering, median filtering, adaptive filtering. | |
| 3. | Spectral analysis and its applications in biosignals. | Autoregressive spectral estimation. | Time-frequency analysis. |
| 4. | Statistical modelling of biological data. Classification problem. | Clustering, regression analysis. | |
| 5. | Performance estimation, hypothesis testing. | Statistical methods in hypothesis testing and performance estimation. | |
| 6. | Computer exercises with Matlab: applying signal processing and analysis methods in real biosignals (EEG, ECG, gene expression data). | Designing and implementing own algorithms for biosignal processing in Matlab. |
Instructions for students on how to achieve the learning outcomes
Exercises 40%, exam 60%. There will be 4 Matlab exercises. The final grade of the course is determined based on the assessment of all part of the course. The weighting factor of each part is given at the beginning of the course.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Partial passing:
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Book | Bioelectrical Signal Processing in Cardiac and Neurological Applications | Leif Sörnmo and Pablo Laguna | Selected chapters from this book. The book selection is tentative and will be confirmed before the course | Yes | English | ||
| Lecture slides | Lceture notes on Processing of Biosignals | Lecturer | Lecture notes + selected extra materials. | Yes | English |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-11000 Basic course in Signal Processing | Advisable | 1 |
| SGN-11006 Basic Course in Signal Processing | Advisable | 1 |
1 . SGN-11000 or SGN-11006 are strongly recommended. Alternatively, equivalent knowledge.
Additional information about prerequisites
Basic skills in digital signal processing and in using Matlab are required.
Prerequisite relations (Requires logging in to POP)
Correspondence of content
| Course | Corresponds course | Description |
|
|
|
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| Lectures Practical works |
Contact teaching: 0 % Distance learning: 0 % Self-directed learning: 0 % |