BIOSIGNAL ANALYSIS

BME524

Course Code: BME524
Semester: -
Theory+Practice: 3 + 0
ECTS: 10

Course Type

Elective Course

Course Level

Master's Degree Without Thesis

Year of Study

Semester

ECTS

Name of Lecturer(s)

Learning Outcomes

At the end of this course, the students;

Learn Biosignals and their properties.
Analysis biosignals with several signal processing algorithms used in engineering field and commenting on results.
Learn new biosignal processing methods and develope new algorithms by using current techniques.
Apply course units in homeworks on MATLAB or similar programs.

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

None

Course Description

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
1^st 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
2^nd Week	Time Domain Filtering. Synchronized varaginf, moving average and derivative based filter types and applications
3^rd Week	Frequency Domain Filtering. Rejecting artefacts with low frequency, high frequency and periodical waveshape. butterworth filters, wiener filter
4^th Week	Event Detection. Detection of P,QRS, T waves from ECG signals, detection of heart sounds from PCG, detection of dicrotic notch from CP
5^th Week	Event Detection. Detection of P,QRS, T waves from ECG signals, detection of heart sounds from PCG, detection of dicrotic notch from CP
6^th Week	QRS Detection Methods. Derivative based methods, Pan-Tompkins algorithm
7^th Week	Detection of EEG waves. Template matching method, coherence analysis, matching filter application
8^th Week	Midterm Exam
9^th Week	Disease Symptoms on Biosignals. Symptoms' wave properties and detecting, miocardial ischemia effects, ectopic beats
10^th Week	Morphological Analysis of ECG.Correlation analysis.
11^th Week	Activation Analysis in Biosignals. Root mean square, zero crossing rate, turn count, form factor calculations
12^th Week	Frequency Domain Charecterization of Biosignals. Fourier spectrum, power spectral density (PSD), periodogram, windowing, PSD measurements
13^th Week	Frequency Domain Charecterization of Biosignals. Fourier spectrum, power spectral density (PSD), periodogram, windowing, PSD measurements
14^th Week	Classification methods. Supervised classification methods, unspervised classification methods

Recommended 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 Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
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 Work Load			287
Total Work Load / 30 (h)			9.56
ECTS Credit of the Course			10

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3	LO4
K1		X	X
K2		X	X	X
K3		X
K4	X	X
K5		X	X
K6
K7	X		X
K8