TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Bachelor's Degree
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	5
NAME OF LECTURER(S)	Associate Professor Aykut Erdamar
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Can calculate the frequency, amplitude and phase responses of the LTI systems. 2) Learn to use the LTI system as a frequency selective filters. 3) Learn the concepts related to physiological signals. 4) Can do advanced mathematical operations associated with biomedical signals and systems. 5) Learn concepts related to design and implement digital filters. 6) Learn the noise filtering and event detection techniques. 7) Learn technical analysis of frequency characteristics, feature detection and waveform / complexity for biomedical signals and systems. 8) Can do one-dimensional signal processing and analysis in laboratory experiments by applying the theoretical knowledge learned in the course.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	Yes(BME428)
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	No
COURSE DEFINITION	In this course, frequency domain analysis of LTI systems, frequency, amplitude and phase responses, use LTI systems as frequency selective filters, bioelectric signals, coupled, concurrent and correlated processes, filtering for removal artifacts, filters used in practice, to determine the important features of biomedical signals, EEG, ECG and PCG parameter detection applications, the waveform and complexity, the approach used in waveform and complexity analysis, frequency characterization, PSD calculation methods and applications issues are handled.
COURSE CONTENTS	WEEK TOPICS 1st Week Frequency domain analysis of LTI systems 2nd Week Frequency, amplitude and phase response 3rd Week Use the LTI system as a frequency selective filters 4th Week Bioelectric signals 5th Week Coupled, concurrent and correlated processes 6th Week Filter design for artifact removal 7th Week Different filters are used in practice 8th Week Midterm 9th Week Feature detection from biomedical signals 10th Week Feature detection applications for EEG, ECG and PCG 11th Week Approaches used in the waveform and complexity analysis 12th Week Frequency Characterization 13th Week PSD calculation methods and applications 14th Week The time-frequency analysis
RECOMENDED OR REQUIRED READING	Digital Sinal Processing, Principles, Algorithms and Applications, 4thEd, J.G.Proakis, D.G.Manolakis, Pearson Education,Inc, Prentice Hall Biomedical Signal Analysis, R.M.Rangayyan, IEEE Press/ Wiley 2002 Discrete-Time Signal Processing, A.V.Oppenheim, R.W.Schafer, Prentice Hall
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Questions/Answers,Problem Solving,Report Preparation,Experiment
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Quiz 5 15 Practice 1 20 Total(%) 65 Contribution of In-term Studies to Overall Grade(%) 65 Contribution of Final Examination to Overall Grade(%) 35 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 1,5 1,5 Preparation for Quiz 5 5 25 Individual or group work Preparation for Final exam 1 30 30 Course hours 14 4 56 Preparation for Midterm exam 1 20 20 Laboratory (including preparation) 8 2,5 20 Final exam 1 2 2 Homework Total Workload 154,5 Total Workload / 30 5,15 ECTS Credits of the Course 5
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No