Course Title: Biomedical Signal Analysis

Part A: Course Overview

Course Title: Biomedical Signal Analysis

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


125H Electrical & Computer Engineering


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


Bundoora Campus


172H School of Engineering


Sem 2 2017

Course Coordinator: Dr Dean Cvetkovic

Course Coordinator Phone: +61 3 9925 9641

Course Coordinator Email:

Course Coordinator Location: 203.3.36

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 be encouraged to apply your knowledge of digital signal processing in the analysis of biomedical signals or biosignals. You will learn about the: analog-to-digital conversion, sampling, windowing, filtering, spectral analysis, wavelet, time-frequency and classification. You will also learn how to calculate, simulate and analyse various biosignals measured from the human body. The theory of this biomedical signal analysis will be explained during the interactive lectures and 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.

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:

  1. Analyse bioelectric signals and measurements using analog-to-digital conversion, sampling and windowing;
  2. Apply cross-correlation and covariance biosignal processing techniques in the analysis of recorded electroencephalographic and electrocardiographic biosignals;
  3. Manage digital filtering, noise reduction, classical and modern spectral analysis, wavelet and time-frequency;
  4. Apply digital filtering and classical spectral analysis to evaluate the electroencephalographic biosignals and the heart rate variability;
  5. Apply modern spectral analysis, wavelet and time-frequency analysis to evaluate the electroencephalographic biosignals;
  6. Apply a range of classification techniques;


Overview of Learning Activities

The learning activities in this course include:

Lectures, Tutorials and Laboratory work:

  • The lectures are designed for you to understand the concepts of modern biomedical engineering signal processing and analysis.
  • The tutorials and laboratory work is designed to give you a 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.


  • The Mini-projects are extensions to the laboratory work undertaken by you. The mini-projects also provide an opportunity to go through structured learning to understand some mathematics and signal processing techniques.
  • Engineering employment requires the capacity to work effectively in teams, to communicate effectively both orally and in writing, and to learn effectively.

Overview of Learning Resources

For the course’s prescribed texts, reference books and other resources such as articles, refer to Part B of the Course Guide and the online learning resources accessible via myRMIT Studies.

All learning resources for this course are available on the blackboard university online system. These resources include:

  • Weekly step by step guidance how to proceed with your study;
  • Theoretical module including PDF document, explaining various topics and ideas;
  • Tutorial exercises including hand written and Matlab script examples with solutions;
  • Laboratory assignment instruction module including written explanations, signal processing examples in Matlab;
  • Laboratory and mini project assignment work has been designed to develop your team and communication skills through written reports and presentations;
  • Mini project work to develop your research skills.


Overview of Assessment

X  This course has no hurdle requirements.

You will be assessed on your knowledge and skills demonstrated from the following deliverables:

Assessment Task 1: Laboratory (reports)

Weighting 20%

This assessment task supports CLOs 1, 2, 3, 4 & 5.

Assessment Task 2: Mini Project (report and presentation)

Weighting 20%

This assessment task supports CLOs 1, 2, 3, 4, 5 & 6.

Assessment Task 3: Lecture Test (written)

Weighting 10%

This assessment task supports CLOs 1, 2, 3 & 4.

Assessment Task 4: Examination (written)

Weighting 50%

This assessment task supports CLOs 1, 2, 3, 4, 5 & 6.