Offered in Fall 2016, 2017
Biosignals & Systems
BMED-310: Dept of Biomedical Engineering, Duquesne University
Instructor: Prahlad G Menon, PhD
Course Description:
BMED-310, Biosignals & Systems is designed to enable students to develop mathematical models for biological systems and for biomedical engineering systems, devices, components, and processes and to use models for data reduction and for system performance analysis, prediction and optimization. The course will focus on the fundamentals of digital signal processing of time series data, via applied exercises and projects with a focus on medical and biological signal analysis and interpretation. My exercises will be based on cardiovascular, gait and balance, electrophysiological (EEG, ECG, etc.), neural signal processing and medical imaging datasets.
Models considered will be drawn from a broad range of applications and will be based on algebraic equations, ordinary differential equations and partial differential equations. The tools of advanced engineering mathematics comprising analytical, computational and statistical approaches will be introduced and used for model manipulation.
The lectures will be accompanied by data analysis assignments using MATLAB.
Learning Objectives
Fundamentals of Bio-Signals & Systems
This course is grounded on learning through programming exercises in Matlab and will offer students a keen intuition in regard to writing effective and efficient code for signal visualization, processing and analysis / classification.
Through extensive exercises involving modifying and extending starter code / programs relating to a range of engineering topics, students will become adept at working with real-world data in a variety of forms.
Topics covered include :
- Continuous-time linear system theory.
- Analysis of continuous-time systems is considered in both the time and frequency domains.
- Linearity, impulse response, convolution, frequency response, filtering, Fourier series, Fourier transforms, sampling theorem, relationship between continuous-time and discrete-time systems (as time perm its), Laplace transforms, system transfer function, poles and zeros, stability. Applications of these techniques will be discussed using examples from circuits, signal processing, communication and control.
Prerequisites: The expected background includes basic algebra, trigonometry, and some familiarity with computers. The course assumes nothing more than this basic background but will supplement early college courses in mathematics and technical computing, including calculus and matrix theory, linear algebra and basic signal processing, owing to the fact that several of these topics will be relevant to the exploration of the mathematical concepts using code in Matlab.
Texts & References:
1) Biosignal and Medical Image Processing, 3rd Edition by John L. Semmlow (Recommended Main Text)
2) Biomedical Signal and Image Processing, Kayvan Najarian, Robert Splinter (Recommended reference text)
3) Digital image processing, Gonzalez, R.C. and Woods,
4) Biomedical Signal Processing: Principles and Techniques By Reddy
5) R.E. Biomedical Digital Signal Processing by Willis J. Tompkins
6) Matlab “Help” documentation!
Syllabus & Course Material, Fall 2016
Available upon request via email; write to: menongopalakrip@duq.edu
or via CourseWeb / Blackboard for registered students.
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