| BIOENGINEERING (ENGLISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| BNG6002 | Analytic Methods in Bioengineering | Spring | 3 | 0 | 3 | 9 |
| Language of instruction: | English |
| Type of course: | Must Course |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Assist. Prof. BURCU TUNÇ ÇAMLIBEL |
| Course Objectives: | The aim of this course is to introduce analytical and numerical methods used to solve problems in bioengineering and to apply these techniques to open-access biomedical datasets using MATLAB. Students will gain computational and interpretative skills through hands-on experience with data analysis, signal processing, optimization, and modeling in a biomedical context. |
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The students who have succeeded in this course; Upon successful completion of this course, students will be able to: 1. Explain fundamental analytical and numerical methods used to solve bioengineering problems. 2. Analyze and interpret open-access datasets related to biomedical engineering. 3. Apply data processing, filtering, modeling, and optimization techniques in MATLAB. 4. Apply analytical methods to real biomedical data to generate meaningful results. 5. Interpret numerical computations and evaluate their accuracy and limitations. 6. Systematically report the analysis process and clearly present the findings. 7. Develop a technical approach for interdisciplinary data analysis in bioengineering. |
| This course focuses on analytical and numerical methods used to solve problems in the field of bioengineering. The course emphasizes practical applications using MATLAB with open-access biomedical datasets. Topics such as biosignal processing, modeling, and data interpretation are integrated with theoretical knowledge, aiming to enhance students' skills in computation, analysis, and scientific reporting. |
| Week | Subject | Related Preparation |
| 1) | Course introduction, MATLAB environment, and overview of open-access biomedical datasets | |
| 2) | Loading, visualizing, and exploring basic time series features of open-access ECG data | |
| 3) | Basic signal processing: Denoising and filtering (Butterworth, moving average) | |
| 4) | RR interval detection and introduction to heart rate variability (HRV) analysis | |
| 5) | Multichannel EEG signal analysis and artifact removal using open datasets | |
| 6) | Basic feature extraction: Mean, variance, energy, and frequency-based features | |
| 7) | Muscle activity analysis using EMG: Contraction timing and RMS computation | |
| 8) | Preparing data for classification: Feature matrix generation and labeling | |
| 9) | Simple machine learning classification (SVM, k-NN) – example case study | |
| 10) | Anomaly detection in biosignal datasets and identification of outliers | |
| 11) | Project planning: Topic selection, dataset identification, and workflow design | |
| 12) | Student project presentations – Applied analysis reports I | |
| 13) | Student project presentations – Applied analysis reports II and final discussion | |
| 14) | Student project presentations – Applied analysis reports III and final discussion |
| Course Notes / Textbooks: | Palm, William J. III – Introduction to MATLAB for Engineers, McGraw-Hill. MATLAB official documentation and online tutorials from MathWorks Open-access biomedical datasets: - PhysioNet (https://physionet.org) - UCI Machine Learning Repository – Biomedical data - Kaggle – Biomedical signal and image processing datasets |
| References: | Palm, William J. III – Introduction to MATLAB for Engineers, McGraw-Hill. MATLAB official documentation and online tutorials from MathWorks Open-access biomedical datasets: - PhysioNet (https://physionet.org) - UCI Machine Learning Repository – Biomedical data - Kaggle – Biomedical signal and image processing datasets |
| Semester Requirements | Number of Activities | Level of Contribution |
| Application | 3 | % 30 |
| Project | 1 | % 10 |
| Midterms | 1 | % 20 |
| Final | 1 | % 40 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Application | 3 | 5 | 15 |
| Study Hours Out of Class | 14 | 8 | 112 |
| Project | 1 | 30 | 30 |
| Midterms | 1 | 10 | 10 |
| Final | 1 | 10 | 10 |
| Total Workload | 219 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Follows scientific literature, analyzes it critically and uses it effectively in solving engineering problems. | 5 |
| 2) | Asks the right questions for scientific innovative designs in the field of Bioengineering, plans, implements, manages and documents innovative work. | 5 |
| 3) | Independently conducts studies in the field of Bioengineering, examines them in depth, takes responsibility and evaluates the results obtained from a critical point of view. | |
| 4) | Presents the results of his/her research and projects effectively in written, oral and visual form in accordance with academic standards. | |
| 5) | Conducts independent research on topics related to Bioengineering that require deep expertise, develops original ideas and transfers this knowledge to practice. | |
| 6) | Uses advanced theoretical and practical knowledge specific to Bioengineering effectively. | 4 |
| 7) | Acts in accordance with professional, scientific and ethical values; takes responsibility by considering the social, environmental and ethical impacts of engineering practices. |