Mar 20, 2023  
2015-2016 Undergraduate Academic Catalog 
2015-2016 Undergraduate Academic Catalog [ARCHIVED CATALOG]

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BE 3915 - Biomedical Combined Laboratory II

1 lecture hours 2 lab hours 2 credits
Course Description
The objective of this course is to introduce the students to specific signal and system analysis tools used in physiological systems evaluation and quantification. The students will be presented with the real-world biomedical engineering problems that overlap the fields of physiology, digital signal processing and biomechanics.  The students will look at problems/laboratories from a joint perspective, which will enable the students to solve multidisciplinary problems in the biomedical engineering field. (prereq: TBD)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
  • Apply fundamental signal processing methodologies to analyze biomedical signals with a goal to extract system-specific pertinent information
  • Apply the appropriate statistics and interpreting their results when analyzing biomedical signals and systems
  • Use MATLAB (and/or other appropriate computer-aided tools) to analyze biomedical signals and systems, and for biomedical system modeling
  • Solve specific biomedical problems using multidisciplinary approach
  • Function on multidisciplinary teams  
  • Document engineering and experimental work

Prerequisites by Topic
  • None

Course Topics
  • Review of cardiovascular physiology
  • Physiological origins of electrocardiographic (ECG) and other electrical cardiac signals
  • Overview of neuroanatomy and neurophysiology
  • Physiological origins of electroencephalographic (EEG) and other electrical neuro-signals
  • Review of joints and muscular system physiology
  • Physiological origins of electromyographic (EMG) and other related signals

Laboratory Topics
  • Analysis of the EEG and local field potentials signals using appropriate signal processing methods
  • Hudgkin and Hoxley neuronal conductance-based model simulation
  • Windkassel aortic flow model simulation  
  • Analysis of ECG, cardiac electrogram, blood pressure, and cardiac cellular signals using appropriate signal processing methods
  • Analysis of EMG signals using appropriate signal processing methods
  • Biomechanics modeling and simulation

Olga Imas

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