Biosignals and Systems
Data acquisition, time and frequency domain analysis, analog and discrete filter design, sampling theory, time-dependent processing, linear prediction, random signals, biomedical system modeling, and stability analysis; introduction to nonlinear systems.
3 credits
Course Outline
This course introduces digital signal processing theory in the context of biomedical applications. Major topics of interest include: Data acquisition, time and frequency domain analysis, analog and discrete filter design, sampling theory, time-dependent processing, random signals, biomedical system modeling, and stability analysis. All methods will be developed to address certain concerns on specific data sets in modalities such as ECG, EEG, EMG, and speech signals. The lectures will be accompanied by data analysis using MATLAB. Students will explore the basics of biosignal processing and gain the hands-on experience with MATLAB® Signal Processing Toolbox by doing a term project. This course is mainly designed for engineering students in their final year of undergraduate studies or in their graduate studies. Engineering students with a strong background in Signals and Systems are well-prepared to take this course. While prior knowledge of Digital Signal Processing and Biomedical Engineering would be useful, it is not essential for a capable student. The course adopts a problem-solving approach, as biomedical engineers are known for their problem-solving skills in clinical settings. The entire course is presented in the form of a series of lectures, real-world biomedical engineering problems, and their solutions.
Prerequisites: |
| ALL of |
| ELEC 221 – Signals and Systems |
| ELEC 341 – Systems and Control |
| ELEC 371 – Biomedical Engineering Instrumentation |
| AND ONE of |
| CPSC 259 – Data Structures and Algorithms for Electrical Engineers |
| CPSC 260 – Data Structures and Algorithms for Computer Engineers |
| AND ONE of |
| MATH 302 – Introduction to Probability |
| MATH 318 – Probability with Physical Applications |
| STAT 251 – Elementary Statistics |
| STAT 302- Introduction to Probability |