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Course Catalog 2011-2012
BME-2626 Processing of Physiological Signals, 5 cr |
Person responsible
Jarno Tanskanen
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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Requirements
Accepted computer assignments and final exam.
Completion parts must belong to the same implementation
Learning outcomes
Student can describe and apply common signal processing, signal analysis, and signal interpretation methods in today's health care and wellness applications. Student can practice the processing and analysis of common physiological signals with Matlab.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Types and origins of physiological signals, and their basic properties. Basics of data acquisition, sampling, and filtering. | Basic digital filter design. | Insights in physiological signal generators. |
| 2. | Basics of EEG signal analysis. Artifact types. Adaptive FIR filtering as artifact rejection method. Basics of spectral and time-frequency analysis. | Basics of wavelet transform. Practical understanding of time-frequency analysis. | Time-frequency distributions. |
| 3. | Basic ECG signal processing and analysis. | Arrhythmia detection. | Nonlinear dynamics. |
| 4. | Computer exercises with Matlab and demonstrations: basic signal processing with Matlab. | Knowledge of several functions in Matlab signal processing toolbox. | Efficient vectorized Matlab signal processing programming. |
Evaluation criteria for the course
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. Grades 1-2: Learning outcomes have been achieved. Satisfactory command in core content of the course. Grades 3-4: Some learning outcomes have been exceeded qualitatively or quantitatively. Good command in core content and complementary knowledge of the course. Good or very good marks from all parts of the course. Grade 5: Most of the learning outcomes have been exceeded. Deep command in the whole content of the course. Almost maximum performance in all parts 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 |
| Lecture slides | English |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-1158 Introduction to Signal Processing, short version | Advisable | |
| SGN-1201 Signaalinkäsittelyn menetelmät | 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 |
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More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| Lectures Excercises |
Contact teaching: 0 % Distance learning: 0 % Self-directed learning: 0 % |