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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EEE 5076 BIOMEDICAL SIGNAL PROCESSING ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR MEHMET KUNTALP

Offered to

Ph.D. in Biotechnology Industrial Ph.D. Program In Advanced Biomedical Technologies Biomedical Tehnologies (English) ELECTRICAL AND ELECTRONICS ENGINEERING BIOTECHNOLOGY ELECTRICAL AND ELECTRONICS ENGINEERING

Course Objective

The course aims to provide the students with a detailed knowledge on various biomedical signals and signal processing techniques used for the analysis of these signals.

Learning Outcomes of the Course Unit

1   Understand different kinds of biological signals that originate from the human body. 2   Use the appropriate signal processing tools to analyze these signals.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Biomedical Signals 2 Preprocessing of the Signal 3 Statistical Analysis 4 Spectral Analysis 5 Nonlinear Analysis 6 Template Matching (Correlation Analysis) 7 Signal Averaging (Improving SNR) 8 Signal Compression (Neural Networks) 9 Morphologic & Transformed Features 10 Postprocessing of Features 11 Feature Selection 12 EEG Signal Processing 13 ECG Signal Processing 14 Midterm

Recomended or Required Reading

to be announced

Planned Learning Activities and Teaching Methods

Research Homework Presentation Report

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 ASG ASSIGNMENT 2 MTE MIDTERM EXAM 3 PRJ PROJECT 4 FCG FINAL COURSE GRADE ASG * 0.30 + MTE * 0.40 + PRJ * 0.30

Further Notes About Assessment Methods

None

Assessment Criteria

Homework, Midterm, and Term Project

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

mehmet.kuntalp@deu.edu.tr

Office Hours

will be posted

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparing assignments 5 12 60 Preparations before/after weekly lectures 12 5 60 Preparation for midterm exam 1 25 25 Midterm 1 4 4 TOTAL WORKLOAD (hours) 188

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 LO.1 2 2 3 2 2 2 LO.2 2 2 4 2 4 1