Course Details

BIOMEDICAL SIGNALS & SYSTEMS

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits
5	BENG410	BIOMEDICAL SIGNALS & SYSTEMS	3+0+0	3	5

Course Details

Language of Instruction	English
Level of Course Unit	Bachelor's Degree
Department / Program	BIOENGINEERING
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	This course provides the knowledge of the origins of bioelectric signals, biomedical signal types, and their properties
Course Content	In the content of this course, artifact removal from biomedical signals: Filtering and time and frequency domain filters, information extraction from the morphology of biomedical signals and frequency characterization of biomedical signals.
Course Methods and Techniques	The course is taught with a student-centered approach. The course includes in-class activities, group work, assignments, mini projects. Various interactive methods such as discussions, peer interaction and hands-on activities will be used.
Prerequisites and co-requisities	None
Course Coordinator	None
Name of Lecturers	Research Assist.Dr. Refika Sultan Doğan https://avesis.agu.edu.tr/refikasultan.dogan refikasultan.dogan@agu.edu.tr
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	TRANQUILLO, J. V. (2013). Biomedical Signals and Systems. SPRINGER.
	Lecture notes and reading materials will be shared weekly through the CANVAS platform. The main textbook is: TRANQUILLO, J. V. (2013). Biomedical Signals and Systems. SPRINGER.
	Lecture notes and reading materials will be shared weekly through the CANVAS platform. The main textbook is: TRANQUILLO, J. V. (2013). Biomedical Signals and Systems. SPRINGER.
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Course Category

Mathematics and Basic Sciences	%10
Engineering	%15
Engineering Design	%10
Social Sciences	%0
Education	%5
Science	%15
Health	%30
Field	%60

Planned Learning Activities and Teaching Methods

Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria

In-Term Studies	Quantity	Percentage
Yarıyıl İçi Çalışmalarının Başarı Notunun Katkısı	1	% 5
Quiz/Küçük Sınav	1	% 10
Ödev	3	% 20
Proje/Çizim	1	% 30
Final examination	1	% 35
Total	7	% 100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Araştırma Ödevi	5	3	15
Tartışma	12	3	36
Yazılı Sınav	1	3	3
Ev Ödevi	1	5	5
Sınıf İçi Aktivitesi	9	3	27
Sunum	1	1	1
Proje	1	10	10
Araştırma	3	2	6
Takım/Grup Çalışması	1	1	1
Yüz Yüze Ders	14	3	42
Final Sınavı	1	4	4
Total Work Load			Number of ECTS Credits 5 150

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Express the basic terminology of signals and systems
2	Apply convolution to continuous-time and discrete-time systems
3	Analyze a system's input-output relationship using Fourier theorem
4	Design and implement simple systems for practical applications in MATLAB
5	Discuss the use of signals and systems for advanced applications
6	Communicate signals and systems concepts through a technical report of a term project

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Origins of bioelectric signals: Transmembrane potentials, ionic currents, resting potential concept
2	Basic electrophysiological terms: Action potential, stimulation types
3	Basic electrophysiological terms: Action potential, stimulation types
4	Fundamentals of biomedical instrumentation: Physiological variables to be monitored, basics of sensors and transducers
5	Fundamentals of biomedical instrumentation: Physiological variables to be monitored, basics of sensors and transducers
6	Basics of heart, brain and muscle anatomy and physiology: Biopotentials and electrode types
7	Basics of heart, brain and muscle anatomy and physiology: Biopotentials and electrode types
8	Cardiovascular, muscular and neurological diseases and related biosignals
9	Cardiovascular, muscular and neurological diseases and related biosignals
10	Properties of bioelectric signals: ECG, EEG, EMG, ENG, EGG
11	Properties of bioelectric signals: ECG, EEG, EMG, ENG, EGG
12	Event detection on biosignals: Derivative based approaches, template matching, matched filter
13	Event detection on biosignals: Derivative based approaches, template matching, matched filter
14	FINAL

Contribution of Learning Outcomes to Programme Outcomes

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=72038&lang=en