| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| BME2011 | Biomedical Signals and Systems | Fall | 2 | 2 | 3 | 6 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi HAKAN SOLMAZ |
| Course Objectives: | The goal of this course is to introduce students to the analysis of continuous and sampled signals using classical techniques including Laplace and Fourier, filter design and artifact removal via filtering, event/abnormality detection, modeling of biomedical systems, and pattern classification and diagnostic decision from biomedical signals. The course includes computer-based assignments and a term project using Matlab for such analyses. |
|
The students who have succeeded in this course; 1. Understand the nature of common biomedical signals 2. Be able to describe biomedical signals mathematically and understand how to perform mathematical operations on biomedical signals 3. Apply the essential techniques for analyzing analog and digital biomedical signals 4. Understand the intuitive meaning of frequency domain and the importance of analyzing and processing signals in the frequency domain 5. Understand the application of Fourier analysis to sampling and filtering of biomedical signals 6. Describe the basics of pattern classification and diagnostic decision from biomedical signals 7. Develop computing skills by using MATLAB for analysis of biomedical signals and modeling of biomedical systems |
| Introduction to Biomedical Signals; Concurrent, coupled, and correlated processes; Filtering for removal of artifacts; Event detection; Waveshape and waveform complexity; Frequency-domain characterization; Modeling biomedical signals; Analysis of non-stationary signals; Pattern Classification and Diagnostic Decision |
| Week | Subject | Related Preparation | |
| 1) | Introduction to Biomedical Signals | ||
| 2) | Concurrent, Coupled, and Correlated Processes | ||
| 3) | Filtering for Removal of Artifacts | ||
| 4) | Filtering for Removal of Artifacts (cont’d) | ||
| 5) | Event/Abnormality Detection | ||
| 6) | Waveshape and Wwaveform Complexity | ||
| 7) | Frequency-domain Characterization | ||
| 8) | Midterm Examination. Discussion and solutions of the questions. | ||
| 9) | Modeling Biomedical Signals | ||
| 10) | Modeling Biomedical Signals (cont’d) | ||
| 11) | Analysis of Nonstationary Signals | ||
| 12) | Pattern Classification and Diagnostic Decision | ||
| 13) | Pattern Classification and Diagnostic Decision (cont’d) | ||
| 14) | Evaluation of Term Projects | ||
| Course Notes: | R.M. Rangayyan, Biomedical Signal Analysis: A Case-Study Approach, 2001 |
| References: |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | % 0 | |
| Homework Assignments | % 10 | |
| Presentation | % 0 | |
| Project | 1 | % 25 |
| Seminar | % 0 | |
| Midterms | 1 | % 25 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 35 | |
| PERCENTAGE OF FINAL WORK | % 65 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Special Course Internship (Work Placement) | 0 | 0 | 0 |
| Field Work | 0 | 0 | 0 |
| Study Hours Out of Class | 16 | 6 | 96 |
| Presentations / Seminar | 0 | 0 | 0 |
| Project | 6 | 2 | 12 |
| Homework Assignments | 0 | 0 | 0 |
| Quizzes | 0 | 0 | 0 |
| Preliminary Jury | 0 | ||
| Midterms | 1 | 3 | 3 |
| Paper Submission | 0 | ||
| Jury | 0 | ||
| Final | 1 | 3 | 3 |
| Total Workload | 156 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |