TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Master's Degree Without Thesis
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	-
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Learn Biosignals and their properties. 2) Analysis biosignals with several signal processing algorithms used in engineering field and commenting on results. 3) Learn new biosignal processing methods and develope new algorithms by using current techniques. 4) Apply course units in homeworks on MATLAB or similar programs.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	None
COURSE DEFINITION	This course introduces advanced techniques of biosignal analysis. Various estimation, detection and filtering methods are descibed and demonstrated on biomedical signals. The methods include harmonic analysis, autoregressive model, Wiener and Matched filters, linear discriminants, and independent components. All methods will be applied on biosignals such as ECG, EEG, EMG, PCG.
COURSE CONTENTS	WEEK TOPICS 1st Week Biosignals and Properties.ECG, PCG, CP, EEG, EMG, ENG, ERP. Introduction to Biosignal Analysis. Objectives and difficulties of biosignal analysis, artefact types on signals nad properties of artefacts 2nd Week Time Domain Filtering. Synchronized varaginf, moving average and derivative based filter types and applications 3rd Week Frequency Domain Filtering. Rejecting artefacts with low frequency, high frequency and periodical waveshape. butterworth filters, wiener filter 4th Week Event Detection. Detection of P,QRS, T waves from ECG signals, detection of heart sounds from PCG, detection of dicrotic notch from CP 5th Week Event Detection. Detection of P,QRS, T waves from ECG signals, detection of heart sounds from PCG, detection of dicrotic notch from CP 6th Week QRS Detection Methods. Derivative based methods, Pan-Tompkins algorithm 7th Week Detection of EEG waves. Template matching method, coherence analysis, matching filter application 8th Week Midterm Exam 9th Week Disease Symptoms on Biosignals. Symptoms' wave properties and detecting, miocardial ischemia effects, ectopic beats 10th Week Morphological Analysis of ECG.Correlation analysis. 11th Week Activation Analysis in Biosignals. Root mean square, zero crossing rate, turn count, form factor calculations 12th Week Frequency Domain Charecterization of Biosignals. Fourier spectrum, power spectral density (PSD), periodogram, windowing, PSD measurements 13th Week Frequency Domain Charecterization of Biosignals. Fourier spectrum, power spectral density (PSD), periodogram, windowing, PSD measurements 14th Week Classification methods. Supervised classification methods, unspervised classification methods
RECOMENDED OR REQUIRED READING	(1)Biomedical Signal Analysis: A Case Study Approach by Rangaraj M. Rangayyan, Wiley Interscience, 2001. (2)Biomedical Digital Signal Processing by Willis J. Tompkins. Prentice-Hall, 1993.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Questions/Answers,Project
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 35 Assignment 1 10 Project 1 10 Total(%) 55 Contribution of In-term Studies to Overall Grade(%) 55 Contribution of Final Examination to Overall Grade(%) 45 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 72 72 Preparation for Quiz 0 0 0 Individual or group work 2 3 6 Preparation for Final exam 1 24 24 Course hours 14 3 42 Preparation for Midterm exam 1 11 11 Laboratory (including preparation) 0 0 0 Final exam 1 96 96 Homework 1 12 12 Project 1 24 24 Total Workload 287 Total Workload / 30 9,56 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No