University of Wisconsin Madison
Computers in Medicine (B M E 463) Syllabus
Course Learning Outcomes
    Course Learning Outcome
  • 1
    Design and implement linear digital filters including integer-coefficient filters for processing biomedical signals including electrocardiograms
  • 2
    Write software for analyzing biomedical signals to find clinically-significant features like the QRS complex in the ECG.
  • 3
    Apply template-matching techniques to biomedical feature recognition.
  • 4
    Implement algorithms designed specifically for biomedical signal data reduction.
Computers in Medicine
B M E 463 ( 3 Credits )
Study of microprocessor-based medical instrumentation. Emphasis on real-time analysis of electrocardiograms. Labs and programming project involve design of biomedical digital signal processing algorithms.
ECE 330, Comp Sci 302
College: College of Engineering
Instructor Name
Instructor Campus Address
Contact Hours
Course Coordinator
Text book, title, author, and year

Biomedical Digital Signal Processing. Tompkins, Willis J., Englewood Cliffs: Prentice Hall, 1993. Out of print (available as pdf on eCOW2 web site.)

Supplemental Materials
Lab Manual used for laboratory portion of course (available on eCOW2 web site).
Lab software: A MATLAB version of software called UW DigiScope (version 3.0) developed for this course is used for labs (available on eCOW2 web site).
Required / Elective / Selected Elective
Selected Elective
ABET Program Outcomes Associated with this Course
Program Specific Student Outcomes
(1) Understanding of biology and physiology as related to biomedical engineering needs.
(2) Ability to apply knowledge of advanced mathematics (including differential equations and statistics), sciences, and engineering to solve problems at the interface of engineering and biology and to model biological systems
Brief List of Topics to be Covered

Electrocardiographic instrumentation concepts. Biomedical digital signal acquisition. Digital filter design including integer-coefficient filters. Signal averaging techniques for biomedical applications. Data reduction techniques for ECGs and other biomedical signals. QRS complex filter design. ECG analysis systems

Additional Information
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