BIOENGINEERING (ENGLISH, THESIS)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
BNG5002	Biomedical Signals and Applications	Fall Spring	3	0	3	8

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :	Assist. Prof. BURCU TUNÇ ÇAMLIBEL
Course Objectives:	This course covers the fundamental characteristics, acquisition methods, processing, and analysis of physiological signals obtained from biological systems (e.g., ECG, EEG, EMG). Students will learn about the analog and digital nature of biomedical signals, noise sources, preprocessing techniques, and time- and frequency-domain analysis methods. The biological significance and clinical relevance of the signals will also be emphasized. Practical sessions using MATLAB or similar software are included.

Learning Outcomes

The students who have succeeded in this course;
Upon successful completion of this course, students will be able to:

1. Describe the structure and characteristics of physiological signals (e.g., ECG, EEG, EMG).
2. Analyze analog and digital representations of biomedical signals.
3. Identify signal acquisition methods and common sources of noise.
4. Apply basic signal processing techniques in both time and frequency domains.
5. Perform preprocessing operations such as filtering, sampling, and transformation.
6. Use software tools (e.g., MATLAB) effectively for signal analysis.
7. Interpret the clinical significance of signals and discuss their applications.
8. Develop small-scale analysis projects on real biomedical signals.
9. Contribute to teamwork and present analysis results in written and oral formats.

Course Content

This course focuses on the structural characteristics, acquisition methods, noise sources, and processing techniques of physiological signals obtained from biological systems (e.g., ECG, EEG, EMG). Emphasis is placed on time- and frequency-domain signal analysis techniques, including digital filtering, Fourier and wavelet transforms, sampling, and quantization. Students learn to extract and interpret meaningful features from biomedical signals while understanding their clinical significance. The course includes practical applications using MATLAB or similar software, and students are encouraged to apply their knowledge through small-scale analysis projects.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to biomedical signals: Definition, classification, and application areas
2)	Fundamental properties of physiological signals: Analog vs. digital, sampling, quantization
3)	Sources of biomedical signals and types of noise (electrical, motion-related, environmental)
4)	Basic concepts of signal processing: Time and frequency domain
5)	Introduction to frequency analysis and the Fourier Transform
6)	Digital filtering techniques: Low-pass, high-pass, and band-pass filters
7)	ECG signals: Structure, acquisition methods, and basic analysis techniques
8)	EEG signals: Brain waves, artifacts, and methods of analysis
9)	EMG signals: Muscle activity, surface vs. needle EMG, and analysis approaches
10)	Time-frequency analysis: STFT and Wavelet transforms
11)	Introduction to feature extraction and classification methods
12)	Software tools for biomedical signal processing (e.g., MATLAB, Python)
13)	Practical project: Signal analysis with real biomedical data (individual or group)
14)	Student presentations, general review, and final evaluation

Sources

Course Notes / Textbooks:	Akay, Metin – Biomedical Signal Processing, Academic Press, 2014. Rangayyan, R. M. – Biomedical Signal Analysis: A Case-Study Approach, IEEE Press, 2nd Edition, 2015.
References:	Akay, Metin – Biomedical Signal Processing, Academic Press, 2014. Rangayyan, R. M. – Biomedical Signal Analysis: A Case-Study Approach, IEEE Press, 2nd Edition, 2015.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Project	1	% 30
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 30
PERCENTAGE OF FINAL WORK		% 70
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	7	98
Presentations / Seminar	1	1	1
Project	1	50	50
Midterms	1	3	3
Final	1	3	3
Total Workload			197

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Follows scientific literature, analyzes it critically and uses it effectively in solving engineering problems.
2)	Designs, plans, implements and manages original projects related to the bioengineering.
3)	Carries out studies related to the bioengineering independently, takes scientific responsibility and evaluates the results obtained from a critical point of view.
4)	Effectively presents the results of his/her research and projects in written, oral and visual form in accordance with academic standards.
5)	Conducts independent research on subjects requiring expertise in the field, develops original thought and transfers this knowledge to practice.
6)	Uses advanced theoretical and practical knowledge specific to the bioengineering field effectively.
7)	Acts in accordance with professional, scientific and ethical values; takes responsibility by considering the social, environmental and ethical impacts of bioengineering practices.