SGN-52606 Processing of Biosignals, 5 cr
Additional information
Basics of signal processing is a pre-requisite for this course.
Person responsible
Ilkka Korhonen, Elina Helander
Lessons
Implementation 1: SGN-52606 2015-01
| Study type | P1 | P2 | P3 | P4 | Summer |
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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 behavioural 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 biomedical or behavioral signal 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 and ECG. - 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 (incl. EEG, ECG). | Designing and implementing own algorithms for biosignal processing in Matlab. |
Instructions for students on how to achieve the learning outcomes
Exercises 20%, exam 80%. 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 | Additional information | Examination material |
| 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 | ||
| Lecture slides | Lceture notes on Processing of Biosignals | Lecturer | Lecture notes + selected extra materials. | Yes |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-11000 Signaalinkäsittelyn perusteet | Mandatory | 1 |
| SGN-11006 Basic Course in Signal Processing | Mandatory | 1 |
1 . Skills to use Matlab are mandatory
Additional information about prerequisites
Basic skills in digital signal processing and in using Matlab are required.
Correspondence of content
| Course | Corresponds course | Description |
| SGN-52606 Processing of Biosignals, 5 cr | BME-2626 Processing of Physiological Signals, 5 cr |