ACADEMICS
Course Details

ELE694 - Biomedical Signal Processing

2022-2023 Fall term information
The course is not open this term
ELE694 - Biomedical Signal Processing
Program Theoretýcal hours Practical hours Local credit ECTS credit
MS 3 0 3 8
Obligation : Elective
Prerequisite courses : -
Concurrent courses : -
Delivery modes : Face-to-Face
Learning and teaching strategies : Lecture, Question and Answer, Drill and Practice, Case Study, Problem Solving
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 : A student completing the course successfully will know the basics of signal processing theory to be used in biomedical studies, Learn the classification of biomedical signals and system modelling approaches, Have basic signal processing tools for the practical biomedical field problems, Efficiently use relevant computer programming tools for developing problem solutions, Learn possible use of artificial intelligence techniques in signal processing and biomedical applications,
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
1 Introduction to biomedical signal processing
2 Classification of biomedical signals
3 Signals and measurements of biological systems: ECG,EEG
4 Signals and measurements of biological systems: EMG, EOG
5 Memory and correlation analysis
6 Continuous time signals and models
7 Discrete time signals and models
8 Midterm Exam I
9 Noise sources in biomedical systems
10 Noise cancellation and signal conditioning
11 Spectral analysis and modeling
12 Midterm Exam II
13 Feature extraction, classification
14 Artificial intelligence in biomedical applications
15 Final exam
16 Final exam
Assessment 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
Quiz 0 0
Midterms 2 40
Final exam 1 40
Total 100
Percentage of semester activities contributing grade success 60
Percentage of final exam contributing grade success 40
Total 100
Workload and ECTS Calculation
Course activities Number Duration (hours) Total workload
Course Duration 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, etc.) 13 4 52
Presentation / Seminar Preparation 0 0 0
Project 0 0 0
Homework assignment 2 20 40
Quiz 0 0 0
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
Key learning outcomes Contribution 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.
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.
3. Follows and interprets scientific literature and uses them efficiently for the solution of engineering problems.
4. Designs and runs research projects, analyzes and interprets the results.
5. Designs, plans, and manages high level research projects; leads multidiciplinary projects.
6. Produces novel solutions for problems.
7. Can analyze and interpret complex or missing data and use this skill in multidiciplinary projects.
8. Follows technological developments, improves him/herself , easily adapts to new conditions.
9. Is aware of ethical, social and environmental impacts of his/her work.
10. Can present his/her ideas and works in written and oral form effectively; uses English effectively.
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest
General Information | Course & Exam Schedules | Real-time Course & Classroom Status
Undergraduate Curriculum | Open Courses, Sections and Supervisors | Weekly Course Schedule | Examination Schedules | Information for Registration | Prerequisite and Concurrent Courses | Legal Info and Documents for Internship | Academic Advisors for Undergraduate Program | Information for ELE 401-402 Graduation Project | Virtual Exhibitions of Graduation Projects | Program Educational Objectives & Student Outcomes | ECTS Course Catalog | HU Registrar's Office
Graduate Curriculum | Open Courses and Supervisors | Weekly Course Schedule | Final Examinations Schedule | Schedule of Graduate Thesis Defences and Seminars | Information for Registration | ECTS Course Catalog - Master's Degree | ECTS Course Catalog - PhD Degree | HU Graduate School of Science and Engineering