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This course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine.
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Description
It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done in MATLAB® during weekly lab sessions that take place in an electronic classroom. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs.
Course content
- Introduction to Biomedical Signal and Image Processing Unlimited
- Chapter 1: data acquisition Unlimited
- Chapter 2: digital filters Unlimited
- Slides: Introduction to Clinical Electrocardiography Unlimited
- Reading Unlimited
- Chapter 3: Fourier representation of signals and systems Unlimited
- Chapter 4: the discrete Fourier transform Unlimited
- Chapter 5: sampling in time and frequency Unlimited
- Chapter 6: Z-transforms Unlimited
- Chapter 7: the short-time Fourier transform Unlimited
- Chapter 8: linear prediction Unlimited
- Chapter 9: image processing Unlimited
- Slides for Lec #10 and #11: decision systems Unlimited
- Probability primer Unlimited
- Venn diagrams Unlimited
- Image processing 2 Unlimited
- Decision systems 2 – density estimation Unlimited
- Introduction to medical image segmentation Unlimited
- Slides Unlimited
- Slides Unlimited
- Medical image modalities Unlimited
- Slides for Lec #18 and #19: random signal processing Unlimited
- Chapter 11: random signals – basic properties Unlimited
- Chapter 12: random signals and linear systems Unlimited
- Lecture slides Unlimited
- Chapter 15: blind source separation Unlimited
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