ELE694 - BIOMEDICAL SIGNAL PROCESSING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| BIOMEDICAL SIGNAL PROCESSING | ELE694 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prequisites | None | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Drill and Practice Case Study Problem Solving | |||||
| Instructor (s) | Department Faculty | |||||
| Course objective | The course objective is to understand the basics of signal processing theory and utilizing some useful signal processing tools and methods efficiently for the signals frequently encountered in the fields of biology and medicine. | |||||
| Learning outcomes |
| |||||
| Course Content | 1. Introduction to biomedical signal processing 2. Classification of biomedical signals 3. Signals and measurements of biological systems: ECG,EEG,EMG 4. Memory and correlation analysis 5. Continuous and discrete models 6. Noise sources in biomedical systems 7. Noise cancellation and signal conditioning 8. Spectral analysis and modeling 9. Feature extraction, classification and artificial intelligence | |||||
| References | Lecture Notes. Bruce, E.N., Biomedical Signal Processing and Signal Modeling, John Wiley & Sons, 2001. Rangayyan, R. M., Biomedical Signal Analysis: A case-study approach, IEEE Press/Wiley Inter-Science, 2002. Oppenheim, A.V., Willsky, A.S., Signals and Systems, 2nd Edt, Prentice-Hall, 1997. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Introduction to biomedical signal processing |
| Week 2 | Classification of biomedical signals |
| Week 3 | Signals and measurements of biological systems: ECG,EEG |
| Week 4 | Signals and measurements of biological systems: EMG, EOG |
| Week 5 | Memory and correlation analysis |
| Week 6 | Continuous time signals and models |
| Week 7 | Discrete time signals and models |
| Week 8 | Midterm Exam I |
| Week 9 | Noise sources in biomedical systems |
| Week 10 | Noise cancellation and signal conditioning |
| Week 11 | Spectral analysis and modeling |
| Week 12 | Midterm Exam II |
| Week 13 | Feature extraction, classification |
| Week 14 | Artificial intelligence in biomedical applications |
| Week 15 | Final exam |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 2 | 20 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 2 | 40 |
| Final exam | 1 | 60 |
| Total | 120 | |
| Percentage of semester activities contributing grade succes | 4 | 60 |
| Percentage of final exam contributing grade succes | 1 | 40 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 13 | 4 | 52 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 2 | 20 | 40 |
| Midterms (Study duration) | 2 | 20 | 40 |
| Final Exam (Study duration) | 1 | 30 | 30 |
| Total Workload | 32 | 77 | 204 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Has general and detailed knowledge in certain areas of Electrical and Electronics Engineering in addition to the required fundamental knowledge. | X | ||||
| 2. Solves complex engineering problems which require high level of analysis and synthesis skills using theoretical and experimental knowledge in mathematics, sciences and Electrical and Electronics Engineering. | X | ||||
| 3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems. | X | ||||
| 4. Designs and runs research projects, analyzes and interprets the results. | X | ||||
| 5. Designs, plans, and manages high level research projects; leads multidiciplinary projects. | X | ||||
| 6. Produces novel solutions for problems. | X | ||||
| 7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects. | X | ||||
| 8. Follows technological developments, improves him/herself , easily adapts to new conditions. | X | ||||
| 9. Is aware of ethical, social and environmental impacts of his/her work. | X | ||||
| 10. Can present his/her ideas and works in written and oral form effectively; uses English effectively | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest