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Department of Biomedical Engineering
BME 427 | Course Introduction and Application Information
| Course Name |
Adaptive Processing of Biomedical Signals
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
BME 427
|
FALL
|
2
|
2
|
3
|
6
|
| Prerequisites | None | |||||
| Course Language | English | |||||
| Course Type | ELECTIVE_COURSE | |||||
| Course Level | First Cycle | |||||
| Mode of Delivery | Face-To-Face | |||||
| Teaching Methods and Techniques of the Course |
Presentation Problem Solving Question/Answer |
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| National Occupational Classification Code | - | |||||
| Course Coordinator |
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| Course Lecturer(s) |
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| Assistant(s) |
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| Course Objectives | The aim of this course is to teach students the filtering methods required for adaptive analysis of biological signals. The modeling of random biological signals, their denoising, Wiener filter theory, and adaptive filter algorithms are covered. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning Outcomes |
The students who succeeded in this course;
|
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| Course Description | This course covers the introduction and modeling of random processes where random biological signals are observed, stationary processes, linear optimum (Wiener) filtering, linear adaptive filtering, steepest descent, LMS and RLS learning algorithms, and Kalman filter theory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Related Sustainable Development Goals |
-
|
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|
|
Core Courses |
|
| Major Area Courses |
X
|
|
| Supportive Courses |
|
|
| Media and Managment Skills Courses |
|
|
| Transferable Skill Courses |
|
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Required Materials | Learning Outcome |
| 1 | Introduction to Random Signal Processing | "Haykin, S. S. (2002). Adaptive filter theory. Pearson Education India. CH 1. | LO1 |
| 2 | Stationary random processes | "Haykin, S. S. (2002). Adaptive filter theory. Pearson Education India. CH 1. | LO1 |
| 3 | Modeling of random processes | "Haykin, S. S. (2002). Adaptive filter theory. Pearson Education India. CH 1. | LO2 |
| 4 | Autoregressive (AR) Model Estimation | "Haykin, S. S. (2002). Adaptive filter theory. Pearson Education India. CH 1. | LO3 |
| 5 | Linear Prediction | "Haykin, S. S. (2002). Adaptive filter theory. Pearson Education India. CH 3. | LO2 |
| 6 | Linear, least squares estimation | "Haykin, S. S. (2002). Adaptive filter theory. Pearson Education India. CH 2. | LO2 |
| 7 | Linear adaptive filtering | "Haykin, S. S. (2002). Adaptive filter theory. Pearson Education India. CH 4. | LO3 |
| 8 | Midterm | - | |
| 9 | Gradient descent learning algorithm | "Haykin, S. S. (2002). Adaptive filter theory. Pearson Education India. CH 5. | LO3 |
| 10 | Least Mean Squares (LMS) learning algorithm | "Haykin, S. S. (2002). Adaptive filter theory. Pearson Education India. CH 6. | LO3 |
| 11 | Least squares (LS) adaptive filters | "Haykin, S. S. (2002). Adaptive filter theory. Pearson Education India. CH 9. | LO3 |
| 12 | RLS learning algorithm | "Haykin, S. S. (2002). Adaptive filter theory. Pearson Education India. CH 10. | LO3 |
| 13 | Adaptive noise cancellation solutions and Biomedical applications | "Haykin, S. S. (2002). Adaptive filter theory. Pearson Education India. CH 13. | LO4 |
| 14 | Kalman and Wiener filter theory | Lecture notes | LO5 |
| 15 | Review | - | |
| 16 | Final Exam | - |
| Course Notes/Textbooks | Haykin S. S. (2002). Adaptive filter theory. Pearson Education India. ISBN: 978-0132671453 |
| Suggested Readings/Materials | - |
EVALUATION SYSTEM
| Semester Activities | Number | Weighting | LO1 | LO2 | LO3 | LO4 | LO5 |
| Homework / Assignments | 1 | 30 | X | X | X | X | X |
| Midterm | 1 | 30 | X | X | X | ||
| Final Exam | 1 | 40 | X | X | X | X | X |
| Total | 3 | 100 |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Participation | - | - | - |
| Theoretical Course Hours | 16 | 2 | 32 |
| Laboratory / Application Hours | 16 | 2 | 32 |
| Study Hours Out of Class | 14 | 2 | 28 |
| Field Work | - | - | - |
| Quizzes / Studio Critiques | - | - | - |
| Portfolio | - | - | - |
| Homework / Assignments | 1 | 20 | 20 |
| Presentation / Jury | - | - | - |
| Project | - | - | - |
| Seminar / Workshop | - | - | - |
| Oral Exams | - | - | - |
| Midterms | 1 | 30 | 30 |
| Final Exam | 1 | 38 | 38 |
| Total | 180 |
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
| # | PC Sub | Program Competencies/Outcomes | * Contribution Level | ||||
| 1 | 2 | 3 | 4 | 5 | |||
| No program competency data found. | |||||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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