Module Guide
Biomedical DSP Programming
Fundamentals
EEB-7-xxx
http://ecce3.lsbu.ac.uk/staff/xiaop/BioDSP/
Faculty of ESBE
2013/14
become what you want to be
Table of contents
1.0
MODULE DETAILS 3
2.0 SHORT DESCRIPTION 3
3.0 AIMS OF THE MODULE 3
4.0LEARNING OUTCOMES 3
4.1 KNOWLEDGE AND UNDERSTANDING ......................................................... 4
4.2 INTELLECTUAL SKILLS.................................................................................. 4
4.3 PRACTICAL SKILLS ........................................................................................ 4
4.4 TRANSFERABLE SKILLS ............................................................................... 4
5.0INTRODUCTION TO STUDYING THE MODULE 4
5.1 OVERVIEW OF THE MAIN CONTENT ........................................................... 4
5.2 OVERVIEW OF TYPES OF CLASSES ........................................................... 4
5.3 IMPORTANCE OF STUDENT SELF-MANAGED LEARNING TIME ............... 6
6.0THE PROGRAMME OF TEACHING, LEARNING AND ASSESSMENT 6
7.0ASSESMENT OF THE MODULE
6
8.0LEARNING RESOURCES
6
8.1 CORE MATERIALS ......................................................................................... 6
8.2 OPTIONAL MATERIALS.................................................................................. 7
NOTES ........................................................................................................................... 7
1.0 MODULE DETAILS
Module Title: Biomedical DSP Programming
Fundamentals
Module Level: M
Module Reference Number: EEB-7-482
Credit Value: 20
Student Study Hours: 150
Contact Hours: 36 hours Teaching/Tutorial, 12 hours
Workshop
Private Study Hours: 114
Pre-requisite Learning (If applicable): None
Co-requisite Units (If applicable): None
Course(s): MSc Biomedical Engineering &
Instrumentation
Year and Semester Semester 1, 2013/14
Module Coordinator: Dr Perry Xiao
UC Contact Details (Tel, Email, Room) Room T215, Tel: 02078157569
Email: xiaop@lsbu.ac.uk
Teaching Team & Contact Details Dr Steve Alty
(If applicable): Office: BR-T 801
Tel: +44 (0)20 7815 7162
Fax: +44 (0)20 7815 7699
E-Mail: steve.alty@lsbu.ac.uk
Subject Area: Telecommunications and Internet
Engineering
Summary of Assessment Method: Exam + Course Work
2.0 SHORT DESCRIPTION
The module covers the fundamentals of biomedical signal processing, the
fundamentals of mathematical algorithms, as well as software programming.
3.0 AIMS OF THE MODULE
The aim of this module is to introduce the fundamentals of probability theory, statistical
tests and procedures and develop the tools needed to understand more advanced
topics such as random sequences, continuous and discrete-time random processes,
and filtering. This module will also introduce the fundamentals of biomedical signal
processing, as well as software programming, including variables, arrays and matrix,
loops and selections, file operations and sub-routines.
4.0 LEARNING OUTCOMES
4.1 KNOWLEDGE AND UNDERSTANDING
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Knowledge of biomedical signal processing, including image processing.
Fundamentals of probability theory, statistical tests and procedures.
Appreciate random sequences, continuous and discrete-time processes, and
filtering.
Fundamentals of software programming including variables, array and matrix,
loops, selections and sub-routines.
4.2 INTELLECTUAL SKILLS
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Understand biomedical signals.
Know how to process biomedical signals.
Understand biomedical data acquisition systems
Understand basics of software programming
4.3 PRACTICAL SKILLS
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Know how to process biomedical signals using Matlab programme.
Know how to use Matlab DSP toolbox and Image processing toolbox.
Implementing signal processing algorithms for Biomedical signals.
4.4 TRANSFERABLE SKILLS
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Communication of observed results in technical format.
Logbook maintaining.
Mathematical manipulation and analysis of data.
5.0 INTRODUCTION TO STUDYING THE
MODULE
5.1 OVERVIEW OF THE MAIN CONTENT
1.
2.
2.
3.
4.
5.
Introduction
Random processes, Analysing variances,
means and counts
Regression and correlation, Discrete-Time Signals and Systems,
and Digital filtering
Biomedical data acquisition systems and Biomedical signal processing
Introduction to Matlab
Practical work (lab sessions)
Introduction – Concepts, history and the background.
Learning outcome
You will be expected to know: the basics of Biomedical DSP Programming.
Random processes, Analysing variances, means and counts – Concepts and the
mathematical background.
Learning outcome
You will be expected to know: the basics of Random processes, Analysing variances, means and
counts.
Regression and correlation, Discrete-Time Signals and Systems, and Digital filtering
– Concepts and the mathematical background.
Learning outcome
You will be expected: to understand the operational principals of Regression and correlation,
Discrete-Time Signals and Systems, and Digital filtering.
Biomedical data acquisition systems and Biomedical signal processing – Concepts,
principles of Biomedical data acquisition systems and Biomedical signal processing.
Learning outcome
You will be expected: to know the principles of Biomedical data acquisition systems,
Biomedical signal processing, including basic image processing.
Introduction to Matlab – The basics of Matlab software and its programming environment.
Learning outcome
You will be expected: to be familiar with Matlab software and its graphical user interface.
Practical work (lab sessions) – Laboratory sessions based on Matlab
Learning outcome
You will be expected to able to develop your own Matlab programmes for DSP, and be able to
programme using following techniques:
o
Variables, arrays and matrices
o
Loops and selections
o
File operations
o
Sub-routines
o
Signal processing.
Tutorial examples
Tutorial examples sheet will be handed out at the end of formal teaching of this study area.
5.2 OVERVIEW OF TYPES OF CLASSES
By lectures, tutorials and workshop exercises
5.3 IMPORTANCE OF STUDENT SELF-MANAGED
LEARNING TIME
In this module, you are required to carry out 114 hours of self study, which is essential
to master the teaching content.
5.4 EMPLOYABILITY
This module will provide students with the basic knowledge of biomedical signal
processing. It can help students to work in the relevant fields of biomedical engineering
and healthcare.
6.0 THE PROGRAMME OF TEACHING,
LEARNING AND ASSESSMENT
Teaching will consist of 2 hour lecture on odd weeks, 2 hour of laboratory work on even
weeks, and there will be 1 hour tutorial each week. Lectures will cover all the main
aspects of the subject matter in the module. Printed material, which will include some
lecture material and tutorial examples will be provided. The laboratory exercises are
designed to supplement the lectures. Lectures and laboratory experiments are treated
as a unified body of work. In addition, you are required to carry out 114 hours of self
managed study.
7.0 ASSESMENT OF THE MODULE
There will be one 2-hour written examination (70%), and 1 coursework assignment
(30%). Each student is expected to maintain a log book on all the lab works. The log
books will be examined periodically during the lab sessions. Each student will be
required to produce one formal written report on the research assignment. You will be
required to submit the reports and logbooks (will be specified in the early part of the
semester) by the final submission date, which will be notified during the semester
allowing you sufficient time to complete your work. You MUST submit your lab work and
assignment, following the standard school procedure, to T314 before the deadline:
Week 12, Friday December 13, 2014, 16:00pm. Late submission will be penalized in
accordance with the University regulation.
8.0 LEARNING RESOURCES
8.1 CORE MATERIALS

Diniz, Paulo S. R., Digital Signal Processing, Cambridge University Press, 2010,
MIL EAN/ISBN: 9781282771345
http://0-lib.myilibrary.com.lispac.lsbu.ac.uk/ProductDetail.aspx?id=277134

Tan, Li, Digital Signal Processing : Fundamentals and Applications, Academic Press,
2007, MIL EAN/ISBN: 9781281056962.
http://0-lib.myilibrary.com.lispac.lsbu.ac.uk/ProductDetail.aspx?id=105696
8.2 OPTIONAL MATERIALS
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P. Armitage, G. Berry, Y. Mathews “Statistical Methods in Medical Research”,
4th Edition, ISBN: 0632052570, Wiley-Blackwell, 2001.
A. Papoulis, “Probability, Random Variables and Stochastic Processes”, 3rd
edition, ISBN: 0070484775, McGraw Hill Higher Education, 1991.
A.V. Oppenheim, R.W. Schafer, J.R. Buck “Discrete-Time Signal Processing”,
2nd edition, ISBN: 0137549202, Prentice Hall, 1998.
Attenborough, Mary P, Mathematics for Electrical Engineering and Computing,
Newnes, 2003, MIL EAN/ISBN: 9781281003034.
http://0-lib.myilibrary.com.lispac.lsbu.ac.uk/ProductDetail.aspx?id=100303
H. More, “Matlab for Engineers”, 2nd edition, ISBN: 0136044220, Prentice Hall,
2008.
D. M. Etter, “Introduction to MATLAB: International Version”, 2nd edition, ISBN:
0132170655, Pearson, 2010.
Hahn, Brian;Valentine, Dan, Essential MATLAB for Engineers and Scientists,
Newnes, 2007, MIL EAN/ISBN: 9781280962325.
http://0-lib.myilibrary.com.lispac.lsbu.ac.uk/ProductDetail.aspx?id=96232
E. Ifeachor, B Jervis, “Digital Signal Processing: A Practical Approach”, 2nd
edition, ISBN: 0201596199, Prentice Hall, 2001.
K. J. Blinowska, J Zygierewicz, “Practical Biomedical Signal Analysis Using
MATLAB”, ISBN: 1439812020, CRC Press, 2011.
I. Sommerville, “Software Engineering: International Version”, 9th edition, ISBN:
0137053460, Pearson, 2010.
Mohapatra, P.K.J., Software Engineering, New Age International, 2010, MIL
EAN/ISBN: 9781282501232
http://0-lib.myilibrary.com.lispac.lsbu.ac.uk/ProductDetail.aspx?id=250123
NOTES
You may notice that this guide states that the module requires 150 study hours,
whereas previous guides have defined each module as 120 study hours. The University
has made this change in line with the way study time is likely to be expressed, in future,
in the majority of Universities. There is no change in teaching time, and no change in
what you are expected to do or achieve. The change concerns the way study time is
measured. Previously, the module was defined as 120 hours work over 12 teaching
weeks. The new measure is still 10 hours per week over 15 weeks, including
assessment.
The workload for a full time student is still expected to be approximately 40 hours per
week.