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\newcommand\semester{Spring 2026}
\newcommand\rsemester{202630}
\newcommand\courseNum{ECE 7251}
\newcommand\courseName{Analysis of Biomedical Signals}
\newcommand\courseCoordinator{Meltem Izzetoglu}
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\newcommand\meetingDesc{One 150-minute lecture}
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\newcommand\supplMaterials{Class slides and handouts.}
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\newcommand\textBookMisc{\\
{\bf Course Reference Texts (Recommended but not Required):} \\
•	Ifeachor E.C., Jervis B.W., Digital Signal Processing: A Practical Approach, 2nd Ed. Prentice Hall, 2002 \\
•	Hayes M.H., Statistical Digital Signal Processing and Modeling, John Wiley \& Sons, 1996. \\
•	Haykin S., Adaptive Filter Theory, Pearson Education Inc., 2002. \\
•	Proakis J. G. \& Manolakis D.G., Digital Signal Processing, 2nd Ed. NY: Macmillan publ., 1992. \\
•	Oppenheim A.V. \& Schafer R.W., Discrete-Time Signal Processing, Englewood Cliffs, NJ: Prentice Hall, 1989 {advanced}. \\
•	Akay M., Biomedical Signal Processing, NY: Academic Press, 1994. \\
•	McClellan J.H., Schafer R.W., Yoder M.A., Signal Processing First, Prentice Hall, 2003. \\
•	Strum R. and Kirk D., Contemporary Linear Systems Using Matlab, PWS, 1994. \\
•	McClellan J.H., Schafer R.W., Yoder M.A.,  DSP First – A Multimedia Approach, Prentice Hall, 1998. \\

{\bf General References and Manuals in Biomedical and Digital Signal Processing} \\
•	Bruce, E.N., Biomedical Signal Processing and Signal Modeling, John Wiley and Sons Inc., 2000. \\
•	Bronzino J. Ed. Biomedical Engineering Handbook, Boca Raton, Fl:CRC Press, 2000. \\
•	Tompkins W.J., Ed. Biomedical Digital Signal Processing, Englewood Cliffs, NJ: Prentice Hall,  1993. \\
•	Cohen A., Biomedical Signal Processing, Boca Raton, Fl:CRC, 1986. \\
•	Oppenheim A.V \& Willsky A.S. with Young I.T., Signals and Systems, Englewood Cliffs, NJ: Prentice Hall, 1983 {introductory} \\
•	Burrus CS., McClellan J.H., Oppenheim A.V., Parks T.W., Schafer R.W., Schuessler H.W., Computer Based Exercises for Signal Processing Using Matlab, Englewood Cliffs, NJ: Prentice Hall, 1994.	\\
}
\newcommand\catalogDesc{Application of signal processing methods to analysis of biomedical signals. Introduction to human physiological signals, including cardiovascular, neurological, hemodynamic and muscular. Consideration of signal processing functions, including biomedical signal acquisition, frequency selective and optimal filtering, modeling and spectrum estimation, stationary and nonstationary signal processing. Pre-requisites: ECE 3225/3245 or ECE 5251 or ECE 7231 or equivalent.}
\newcommand\preReqs{Electrical \& Computer Engr 3225 Or Electrical \& Computer Engr 3245 Or Electrical \& Computer Engr 5251 Or Electrical \& Computer Engr 7231}
\newcommand\coReqs{None}
\newcommand\coreRequirement{ECE Graduate Course}
\newcommand\courseExpectation{Students will: \\
1) Learn the basis of biomedical signal processing to: \\
•	Model biomedical signals using statistical methods in order to analyze them. \\
•	Acquire and process statistically stationary as well as non-stationary biomedical signals to extract desired information. \\
•	Identify and separate desired and unwanted components of a signal. \\
2) Study: \\ 
•	The nature of physiological phenomena responsible for generating the signal, gain insight about the identification of an appropriate model for the signal. \\
•	The nature of the underlying physiological and physical processes based either on observation of the signal or on observation of how the process alters its characteristics. \\
3) Gain hands-on experience with simulated and actual biomedical signals using MATLAB and other computational tools. \\
}
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\newcommand\covTopics{\item Human physiology and Biomedical signal acquisition 
\item Sampling and Quantization
\item Discrete time signals, systems and transforms
\item Filter design
\item Stochastic processes
\item Power spectral density (PSD) estimation
\item Introduction to Optimal filtering, Blind Source Separation, Time-frequency analysis
}
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\caption*{Tentative Schedule for \textbf{All Sections}} \vspace{0.05in}
{\renewcommand{\arraystretch}{1.5} \small
\begin{tabularx}{\linewidth}{c|l}
\toprule
\large \textbf{Week} & \large \textbf{Lecture Content}\\
\midrule \midrule
1-2 & Intro to Biomedical Signals: Acquisition \& Processing, HW/Workshop 1\\
3-4 & Sampling and Discrete Time Signals \& Systems,  HW/Workshop 2
HW/Workshop 2\\
5-7 & Transforms: z, DTFT, DFT, FFT, HW/Workshop 3
HW/Workshop 3\\
7 & Midterm Exam\\
8 & Basic Filtering Techniques: Pole-zero placement, HW/Workshop 4\\
9-10 & Basic Filtering Techniques:  FIR \& IIR design, HW/Workshop 5\\
11 & Review of Stochastic Processes\\
12-13 & PSD Estimation: Parametric/Non-Parameteric Methods, HW/Workshop 6\\
14 & Intro to Optimum Filters, Blind Source Separation, Time-Frequency Analysis\\
15 & Final Assignment/Report Submission\\
\bottomrule \end{tabularx} } \end{table}}
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Your final grade will be determined from the following: \\
•	Homework Assignments \& Computer Workshops (50\%) \\
•	Midterm Exam (25\%) \\
•	Final Assignment (25\%) \\
\\
Letter grade scale: A(93--100), A--(90--92), B+(87--89), B(83--86), B--(78--82), C+(74--77),\\
C(70--73), F(<70)}
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{\bf Homework and Workshops:} Homework assignments and workshop reports will be due back on the following week after they are assigned. Late penalty will be applied on each of the late days following the due date where 5 points/day will be taken out of the overall grade received. \\
\\
{\bf Midterm Exam:} There will be one midterm exam which will be taken in class or as a take home exam.\\
\\
{\bf Final Assignment:} There will be one final assignment on a comprehensive topic where students have to prepare a report to be submitted during finals week. \\
\\
{\bf Miscellaneous (please read this carefully!) }\\
Homework/workshop and final assignments and reports you turn in should be performed individually, written out on your own, and not copied verbatim from another student's work or from the material found Online. It should reflect your understanding of the material. Homework problems/Workshop, final assignment reports which are turned in and found to be verbatim copies of each other will be given zero credit, regardless of which is the original work. 
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Since you opted for an AI Policy, you should edit this part, choosing one of the following statements, modifying as desired:\\
\\
The use of AI-generated content is not permitted in this course.
Its use will result in an academic integrity violation and a zero on the assignment.\\
\\
OR\\
\\
The use of AI-generated content is allowed in this course.\\
\\
OR\\
\\
The use of AI-generated content is permitted as follows: (a) for generating a first draft or (b) for generating an outline or (c) for generating XXX.\\
\\
AND, if AI is allowed:\\
\\
Even if you have significantly edited AI-generated material, you must identify the AI tool used to assist in generating your work. You are required to provide the name of the tool, date used, and prompts used to generate the output.  As you may be required to submit the original AI output, you must keep a copy of the original output and provide it when requested.  If questions arise about the authorship of submitted work, you are responsible for authenticating your authorship.  You should save evidence of your authorial process, such as drafts, notes, version histories, and complete transcripts of AI assistance.
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