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 |
Course Coordinator: Dr Shaun Cloherty
Course Coordinator Phone: +61 3 9925 0424
Course Coordinator Email: shaun.cloherty@rmit.edu.au
Course Coordinator Location: 12.08.017
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. You will learn about:
- Different types of biomedical data recordings;
- Signal acquisition and analog-to-digital conversion;
- 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 data;
- How to select and apply these techniques to analyse various biomedical signals measured from the human body.
The theory related to biomedical signal analysis will be explained during the interactive lectures and interactive tutorial sessions. There will be opportunities for you to apply this knowledge in practical laboratory exercises and group mini-projects and to engage in one-on-one and group discussions.
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.
On completion of this course, you should be able to:
- Explain acquisition of biomedical signals and measurements using analog-to-digital conversion, sampling and quantization.
- Identify different types of biomedical signals and sources of variability and noise in the data.
- Select and manage appropriate choices of digital filtering, and noise reduction as required.
- Implement spectral analysis and time-frequency analysis to evaluate biomedical signals.
- Apply a range of classification techniques.
Overview of Learning Activities
The learning activities in this course include:
- Recorded lectures designed to introduce you to concepts of biomedical signal processing and analysis.
- Tutorials which will give you the opportunity to complete worked examples relating to concepts introduced in the lectures.
- Laboratories designed to give you hands-on experience of the MATLAB signal processing tools available. These have been specifically designed to help you understand the theory and applications without the mathematical rigours associated with the tools.
- Mini-projects which are extensions to the laboratory work undertaken by you. The mini-projects also provide an opportunity for structured learning to aid your understanding of the mathematics and signal processing techniques.
- Problem sheets are designed to help develop your understanding of how and when to apply different techniques and provide regular feedback on your progress.
Overview of Learning Resources
The learning resources for this course include:
- Weekly step-by-step guidance, on Canvas, on how to proceed with your study.
- Lecture material prepared by teaching staff explaining various topics and ideas.
- Tutorial exercises MATLAB script examples.
- Laboratory assignment instructions including written explanations and examples in MATLAB.
- Mini-project work to develop your research skills as well as your team and communication skills through written reports and presentations.
- Required software (MATLAB) is freely available to RMIT students.
Overview of Assessment
X This course has no hurdle requirements.
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You will be assessed on your knowledge and skills demonstrated from the following deliverables:
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