Course Title: Biomedical Signal Analysis and Image Processing

Part A: Course Overview

Course Title: Biomedical Signal Analysis and Image Processing

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

EEET1417

City Campus

Undergraduate

125H Electrical & Computer Engineering

Face-to-Face

 Sem 2 2006,
Sem 2 2007,
Sem 2 2009,
Sem 2 2011,
Sem 2 2015,
Sem 2 2016

EEET2494

Bundoora Campus

Undergraduate

172H School of Engineering

Face-to-Face

 Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 2 2020,
Sem 2 2021,
Sem 2 2022,
Sem 2 2023

Course Coordinator: Dr Shaun Cloherty

Course Coordinator Phone: +61 3 9925 0424

Course Coordinator Email: shaun.cloherty@rmit.edu.au

Course Coordinator Location: 012.08.017

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

You should have successfully completed the course EEET2369 Signals and Systems, an equivalent course, or provide evidence of equivalent capabilities.


Course Description

In this course you will develop your knowledge of digital signal processing (DSP), building upon the skills acquired in Signals and Systems (EEET2369). This course will introduce you to practical applications of DSP in the analysis of biomedical signals and images. You will learn about:

  • Different types of biomedical signals and imaging techniques;
  • Biomedical signal and image acquisition;
  • Time domain analysis of discrete signals;
  • Design of digital filters;
  • Frequency domain analysis of discrete signals;
  • Time-frequency analysis of non-stationary signals;
  • Classification of signals and images;
  • How to select and apply these techniques to analyse various biomedical signals.

The theory related to biomedical signal analysis will be presented through a series of lectures and lectorial sessions. You will apply this knowledge in a series of practical laboratory exercises and a group project. 

Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. (This applies to students who commence enrolment in a bachelor honours program from 1 January 2016 onward. See the WAM information web page for more information.) 


Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes of the Bachelor of Engineering (Honours):

1.1 Comprehensive, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline.

1.2 In-depth understanding of specialist bodies of knowledge within the engineering discipline.

1.3 Discernment of knowledge development and research directions within the engineering discipline.

2.1 Application of established engineering methods to complex engineering problem solving.

2.2 Fluent application of engineering techniques, tools and resources.

3.2 Effective oral and written communication in professional and lay domains.

3.6 Effective team membership and team leadership.

 


Upon successful completion of this course, you will be able to:

  1. Explain acquisition of biomedical signals and measurements using analog-to-digital conversion, sampling and quantization.
  2. Identify different types of biomedical signals and sources of variability and noise in the data.
    3. Identify applications of signal processing in science and engineering.
    4. Select an appropriate approach to a given signal processing task and critically evaluate alternative solutions.
    5. Implement a range of digital signal processing operations for analysis of biomedical signals with a high level of proficiency.
    6. Demonstrate a high level of self-directed learning ability and good oral and written communication skills on technical signal processing topics.

 


Overview of Learning Activities

Student learning occurs through the following experiences and evaluation processes:

  • Recorded lectures where syllabus material will be presented and explained, and the subject will be illustrated with demonstrations and examples.
  • Completion of tutorial questions and laboratory projects which provide an introduction to software tools for design, simulation and evaluation of signal and image processing systems, and are designed to give further practice in practical application of the course material and provide feedback on student progress and understanding.
  • Self-directed private study and problem-based learning, working through the course material as presented in class and learning materials, and gaining practice at solving conceptual and numerical problems.

Feedback will be provided throughout the semester in class and/or online discussions, through individual and group feedback on practical exercises and by individual consultation.


Overview of Learning Resources

You will be expected to use library and electronic resources (as well as any other appropriate resources) to engage in professional reading and private study of relevant material on biomedical signal and image processing.

The learning resources for this course include:

  • Lecture material prepared by teaching staff.
  • Recommended textbook and references as listed in the Course Guide Part B and the RMIT online teaching platform.
  • You will be expected to have access to suitable computing equipment for design and evaluation of signal and image processing systems. Required software (MATLAB) is freely available to RMIT students.

 


Overview of Assessment

X  This course has no hurdle requirements.

Assessment for this course consists of the following components:

  • Laboratory Tasks.
  • Laboratory Group Project.
  • Problem Sheets.

You will be required to submit formal individual reports for each laboratory task. Feedback will be provided via Canvas or via individual consultation. During the laboratory sessions the tutor will provide further insight into your work and how it could potentially be improved or expanded.

Assessment Task 1: Laboratory Tasks
Weighting 40%
This assessment task supports CLOs 1, 2, 3, 4 & 5. 

Assessment Task 2: Laboratory Group Project
Weighting 30%
This assessment task supports CLOs 1, 2, 3, 4, 5 & 6. 

Assessment Task 3: Problem Sheets
Weighting 30%
This assessment task supports CLOs 1, 2, 3, 4 & 6.