Course Catalog 2014-2015
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Course Catalog 2014-2015

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

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises
Assignment



 



 
 4 h/week
 2 h/week
 40 h/per



 



 
SGN-52606 2014-01 Thursday 14 - 17 , TC219
Monday 14 - 16 , TB224
Thursday 14 - 17 , TB222

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 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:

Completion parts must belong to the same implementation

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-12000 Kuvan- ja videonkäsittelyn perusteet Advisable    

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 
SGN-52606 Processing of Biosignals, 5 cr BME-2626 Processing of Physiological Signals, 5 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-52606 2014-01   Lectures
Excercises
   
Contact teaching: 0 %
Distance learning: 0 %
Self-directed learning: 0 %  

Last modified17.10.2014