| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Can calculate the frequency, amplitude and phase responses of the LTI systems.
- Learn to use the LTI system as a frequency selective filters.
- Learn the concepts related to physiological signals.
- Can do advanced mathematical operations associated with biomedical signals and systems.
- Learn concepts related to design and implement digital filters.
- Learn the noise filtering and event detection techniques.
- Learn technical analysis of frequency characteristics, feature detection and waveform / complexity for biomedical signals and systems.
- 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 |
|---|
| Prerequisites | Yes(BME345) |
|---|
| Recommended Optional Programme Components | No |
|---|
| Course Description | 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 |
|
| Recommended 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 Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| 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 Work Load | 154.5 |
| Total Work Load / 30 (h) | 5.15 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
