July 17, 2017
WELDON SCHOOL OF BIOMEDICAL ENGINEERING
Biomedical Signal Processing
BME 595 - Fall 2017
Registration: Biomedical Signal Processing
12890 - BME 59500 - MJ4 (or)
13563 - BME 59500 - EP5 (Distance Learning)
Time: MWF 12:30 pm { 1:20 pm
Location: MJIS 1083
Credits: 3
Instructor: Hari M. Bharadwaj, Ph.D.
Assistant Professor of Biomedical Engineering
Assistant Professor of Speech, Language, & Hearing Sciences
Oce: LYLE 3162
e-Mail: hbharadwaj@purdue.edu
Oce Hours: TBD in LYLE 3162
Description: An introduction to the application of digital signal processing and statisti-
cal techniques to practical problems involving biomedical signals and sys-
tems. Topics include: overview of biomedical signals; Fourier/Z-transforms
review and lter design, linear-algebraic view of ltering for artifact removal
and noise suppression (e.g., noise cancellation, PCA and other signal-space-
projection methods) for univariate and multivariate measurements (e.g.,
multichannel electrophysiology, fMRI); time-domain analysis and statisti-
cal inference on signal features (e.g., ECG signals associated with cardiac
anomalies, event-related brain responses); frequency-domain characteriza-
tion of signals and systems; modeling biomedical time series (e.g., AR mod-
els) and systems (e.g., deconvolution), and model-based ltering (e.g., EEG
inverse-problem, brain-computer-interface); analysis of non-stationary sig-
nals; pattern classi cation and diagnostic decisions. A \hands-on" approach
is taken throughout the course (see section on required software).
Course Outcomes: 1. Understand practical problems in objective analyses of biomedical signals.
2. Understand the theoretical background underlying the use of digital signal
processing techniques for biomedical applications.
3. Understand the practical bene ts and limitations of various digital signal
processing approaches and identify the best solution for speci c problems.
4. Implement appropriate signal processing algorithms for practical prob-
lems involving biomedical signals and systems.
5. Propose, carry out, orally present, and write up in conference-proceedings
format, a biomedical-research mini project using signal-processing.
Learning Strategies: In-class lectures and discussions; Hand-solved and computer-programmed
problem sets; Research-oriented prede ned midterm project; Self-designed
nal research project
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Prerequisites: ECE 301 or equivalent (Signals and Systems), ECE 302 or equivalent
(Probability and Random Processes), Familiarity with either MATLAB™ or
Python™. If you are concerned about whether you have the requisite back-
ground, feel free to get in touch me in advance, or within the rst week of
classes.
Textbook: Rangayyan, R. M. (2015). Biomedical signal analysis (2nd Edition). Wiley-
IEEE Press. ISBN: 0470911396 (Online ISBN 1119068129). [Online access
available through Purdue Libraries]
Supplemental
References: 1. Moon, T. K., & Stirling, W. C. (2000). Mathematical methods and algo-
rithms for signal processing. Prentice hall. ISBN: 0201361868.
2. Brillinger, D. R. (2001). Time series: data analysis and theory. Society
for Industrial and Applied Mathematics. ISBN: 0898719240.
3. Mitra, P., & Bokil, H. (2008). Observed brain dynamics. Oxford Univer-
sity Press. ISBN: 0195178084.
4. Oppenheim, A. V., Schafer, R. W., & Buck J. R. (1999). Discrete-Time
Signal Processing (2nd Edition). Prentice Hall. ISBN: 0072817259.
Required Software: 1. MATLAB™ ( R2014a) or Python™ (2.7.x) { Pick either
2. Any software to typeset PDF documents (e.g., Microsoft Word™ ! con-
vert/print to PDF, or LATEX)
3. For distance learning students only: Software and equipment to shoot an
unedited 12 min long digital video in a suitable environment for the oral
presentation of the nal project (e.g., using a smartphone).
Blackboard: Materials and grades for the course will be posted on Blackboard. Besides
instructor-posted material, one component of Blackboard that will be use-
ful is the Online Discussion Board. Students are encouraged to use and
contribute to (i.e., post response if you know the answer to \how to"-type
questions) this resource for sharing general and speci c MATLAB/Python
knowledge across the class. However, please note: no sharing of code blocks
or explicit solutions to homework sets (See note on collaboration below).
Also, online fora such as Signal Processing Stack Exchange may already
have answers to questions that typically come up. However, note that it
is possible for online resources to have erroneous information. Learning to
evaluate and use crowd-sourced online references is an important skill in the
modern-day signal processing practitioner's repertoire.
Final Grade
Composition:
Assessment Item Weight
Problem Sets (6) 45%
Midterm (group or individual) project 20%
Final (individual) research project:
- Content 15%
- Oral presentation 10%
- Written presentation 10%
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Final Project: ( nal 5 weeks) An independent project will apply signal processing to a
research question of interest to each student. This project can either be
related to ongoing research in a lab or can replicate a published study. The
nal projects are intended to be extensive as they will hopefully be in an area
of direct interest and familiarity to each student. Projects will be presented
to the class during the nal two weeks of the semester (modeled after a 15-
min \presentation + questions" conference talk) and will be written up in
a nal report (modeled after a conference proceedings paper, 6-10 pages).
Grading is based on content, oral presentation, and written presentation.
Note: content is judged based on what you accomplish by submission of
your written report, i.e., you are welcome to keep working after your oral
presentation and include a more complete version in your written report.
Access to MATLAB: It is the responsibility of each student working with MATLAB to nd a
reliable computing environment in which to do the work for this course.
This should be worked out within the rst week of class. MATLAB should
be available on all ITAP machines on campus, as well as via the remote Citrix
server. MATLAB tutorials will be posted on Blackboard. Oce hours and
the Online Discussions are available to supplement your existing familiarity
with MATLAB.
Access to Python: It is the responsibility of each student working with Python to nd a reliable
computing environment in which to do the work for this course. This should
be worked out within the rst week of class. Please use Python 2.7 and
associated scienti c modules (mainly NumPy, SciPy, matplotlib, pandas,
and scikit-learn) from the Anaconda distribution. Excellent online tutorials
are available for scienti c Python. Oce hours and the Online Discussions
are available to supplement your existing familiarity with Python.
Academic integrity
& Collaboration: See https://www.purdue.edu/odos/academic-integrity/ { \Purdue Univer-
sity values intellectual integrity and the highest standards of academic con-
duct. To be prepared to meet societal needs as leaders and role models, stu-
dents must be educated in an ethical learning environment that promotes
a high standard of honor in scholastic work. Academic dishonesty under-
mines institutional integrity and threatens the academic fabric of Purdue
University. Dishonesty is not an acceptable avenue to success. It dimin-
ishes the quality of a Purdue education, which is valued because of Purdue's
high academic standards. Fostering an appreciation for academic standards
and values is a shared responsibility among students, faculty, and sta . The
information on this website is directed to students to de ne academic dis-
honesty and how to avoid it."
The skills to be learned in this class rely on each student doing and under-
standing the assignments themselves. However, collaboration is encouraged
in ways that help to avoid getting stuck with procedural issues or software
quirks (e.g., not knowing the name of a speci c toolbox function, etc.).
However, in no case should solutions, code, or text be copied from another
student. Each student is expected to write their own code, solutions, and
text. Violations of this expectation will result in 0 credit.
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Tentative Schedule: Topics will be discussed using representative biomedical examples. The structure
is tentative and will likely evolve depending on student reception.
Date Topic Reading Assignments
Aug 21{25 Background review; Orientation to MATLAB and
Python; Introduction to biomedical signals and systems
Ch 1 & 2
Aug 28 { Sep 01 Filtering and Artifact Suppression I { Averaging; Filter
design & trade-o s
Ch 2 & 3 PS 1 due
Sep 04 Labor Day { No Class
Sep 06 { 08 Filtering and Artifact Suppression II { Filtering beyond
just frequencies (linear algebraic view), noise \cancella-
tion" and regression, optimal ltering
Ch 3
Sep 11 { 15 Filtering and Artifact Suppression III { Multichannel l-
tering, PCA; Additional biomedical examples of ltering
and artifact removal
Sep 18 { 22 Time-domain analysis I { Feature enhancement; Statisti-
cal inference on signal data, ROC curves
Ch 4 PS 2 due
Sep 25 Midterm Project overview { Cochlear Implant simulation
-OR- EEG processing and inference
Sep 27 { 29 Time-domain analysis II { Template based analysis; De-
tecting changes in signal statistics; Relating signal pa-
rameters and physiology
Ch 4 & 5 PS 3 due
Oct 02 { 06 Frequency-domain analysis I { Spectra and correlation
functions of random processes; Spectrum Estimation
Ch 6
Oct 09 October Break { No Class Midterm
project due
Oct 11 { 13 Frequency-domain analysis II { Coherence, phase locking Ch 6
Oct 16 { 20 Modeling of biomedical signals and systems I { Deconvo-
lution; Regularization and model selection
Ch 7 PS 4 due
Oct 23 { 27 Modeling of biomedical signals and systems II { Autore-
gressive models and applications
Ch 7
Oct 30 { Nov 03 Multiple comparisons problem; Non-parametric inference
Nov 06 { 08 Analysis of non-stationary signals; Hilbert transform;
Linear algebraic view of wavelet analysis
Ch 8 PS 5 due
Nov 10 Hari away { No Class Project
plan due
Nov 13 Point processes and spiking data
Nov 15 Deconvolution revisited; Spike- eld coherence
Nov 20 Pattern classi cation and diagnostic decisions I { ROC
curve review; Optimal detectors
Ch 9 PS 6 due
Nov 22 { 24 Thanksgiving { No Class
Nov 27 { Dec 01 Pattern classi cation and diagnostic decisions II { Clus-
tering and \Supervised" learning
Ch 9
Dec 04 Review and consolidation
Dec 06 { 08 No Class { Work on nal project
Dec 11 { 15 Final project presentations during exam week (no exam) Final report
due
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Problem Sets &
Project Submissions: Submitted work will consist of both written and electronic les. All assign-
ments are due by the beginning of class on the due date. Written material
will consist of derived (handwritten) solutions and/or PDF documents with
inserted gures. Hand written work should be handed in at the beginning
of class. Electronic material will consist of your *.PDF le, and any scripts
or functions you have written, as well as any *.mat data les1 you have gen-
erated. The *.PDF le must have a list of every electronic le submitted
with the assignment, with a brief description. Every gure in your *.PDF
le should be generated by the code you submit, i.e., I should be able to
run your code and see your gures appear with no e ort. All electronic les
should be bundled together as one ZIP archive before submitting.
There is a quota of three total late days you can use throughout the semester
to manage unanticipated pressing events that may prevent you from submit-
ting assignments on time. Save and use them judiciously. You can use all
three days for one assignment or spread them over two or three assignments.
Note however that part-day delays (e.g., late by 1 hour) will count as 1
whole day of quota being used up. No submissions will be accepted, even if
you have unused late days, after Dec 15, 2017 (last day of the exam week).
Exceptions will be made to the 3-day rule only in extreme circumstances
(e.g., extended incapacitating medical issue) about which you discuss with
me as soon as reasonably possible, and make alternate arrangements.
Students with
disabilities: Students with disabilities must be registered with Adaptive Programs in
the Oce of the Dean of Students before classroom accommodations can be
provided. If you have a disability that requires academic adjustments, please
make an appointment with me to discuss your needs as soon as possible.
In the event of a major campus emergency, course requirements, deadlines and grading percentages
are subject to changes that may be necessitated by a revised semester calendar. Ways to get information
about changes in this course: Blackboard web page, my email address: hbharadwaj@purdue.edu. Addi-
tional suggestions for best practices in the case of a campus emergency are available at:
http://www.purdue.edu/ehps/emergency preparedness/ (e.g., sign-up for emergency text messages).
