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Curricular information is subject to change
On successful completion of this subject the student will be able to:
• Explain basic principles of how an analog sensor signal can be converted reliably to a digital format for processing
• Translate signal representations between the time and frequency domains
• Design and implement digital filters and use them to remove noise or improve signal detection
• Describe and know how to address the core challenges in biomedical signal analysis, such as the interference of noise and artifacts in the signals
• Perform basic feature extraction and pattern classification for diagnostic applications
- Basics of Biomedical signal processing
- Fourier Transform
- Sampling
- Linear systems
- Z-transform
- Digital Filters design
- De-noising signals and signal manipulation
- Feature extraction from biomedical signals
- Introduction to classification and machine learning
- Matlab programming
Student Effort Type | Hours |
---|---|
Lectures | 23 |
Computer Aided Lab | 20 |
Autonomous Student Learning | 60 |
Total | 103 |
Sufficient mathematics background to be comfortable with integral calculus, probability theory, and basic linear algebra
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment: Assignments and class activities | Throughout the Trimester | n/a | Graded | No | 30 |
Project: Project | Throughout the Trimester | n/a | Graded | No | 20 |
Examination: 2-hour End of Semester Exam | 2 hour End of Trimester Exam | No | Graded | No | 50 |
Resit In | Terminal Exam |
---|---|
Spring | Yes - 2 Hour |
• Feedback individually to students, post-assessment
Not yet recorded.
Computer Aided Lab | Offering 1 | Week(s) - Autumn: All Weeks | Fri 13:00 - 14:50 |
Lecture | Offering 1 | Week(s) - 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 | Mon 14:00 - 14:50 |
Lecture | Offering 1 | Week(s) - Autumn: All Weeks | Thurs 11:00 - 11:50 |