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
In this course we will focus on the basic concepts, methodologies and tools of biosignal processing. This course introduces basic 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, introduction to Wavelet, linear prediction, random signals, biomedical system modeling, and stability analysis; introduction to nonlinear systems.
All methods will be developed to address certain concerns on specific data sets in modalities such as EEG, speech signal, fMRI. The lectures will be accompanied by data analysis assignments using MATLAB. Students will explore the basics of biosignal processing and gain the hands-on experience with MATLAB® Signal Processing Toolbox by doing homework assignments and a term project.
Course Topics
- Introduction and basics
- Data acquisition (sampling and reconstruction of signals)
- Discrete-time signals and systems
- Discrete fourier transform (DFT)
- Digital Filter Design
- Multirate digital signal processing
- Random variables and stochastic processes
- Examples of biomedical signal processing
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 |
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