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COURSE UNIT TITLECOURSE UNIT CODESEMESTERTHEORY + PRACTICE (Hour)ECTS
BIOSIGNAL ANALYSIS BME524 ------- 3 + 0 10

TYPE OF COURSE UNITElective Course
LEVEL OF COURSE UNITMaster's Degree Without Thesis
YEAR OF STUDY-
SEMESTER-------
NUMBER OF ECTS CREDITS ALLOCATED10
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 DELIVERYFace to face
PRE-REQUISITES OF THE COURSENo
RECOMMENDED OPTIONAL PROGRAMME COMPONENTNone
COURSE DEFINITIONThis 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
WEEKTOPICS
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 METHODSLecture,Questions/Answers,Project
ASSESSMENT METHODS AND CRITERIA
 QuantityPercentage(%)
Mid-term135
Assignment110
Project110
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 exam17272
Preparation for Quiz000
Individual or group work236
Preparation for Final exam12424
Course hours14342
Preparation for Midterm exam11111
Laboratory (including preparation)000
Final exam19696
Homework11212
Project12424
Total Workload287
Total Workload / 309,56
ECTS Credits of the Course10
LANGUAGE OF INSTRUCTIONTurkish
WORK PLACEMENT(S)No
  

KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
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K2    X   X   X
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K8