42906 Biomedical Signal Processing
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Credit points: 6 cp
Subject level: Postgraduate
Result type: Grade and marksRequisite(s): (((48541 Signal Theory OR 48540 Signals and Systems OR 41090 Information and Signals OR 41162 Fundamentals of Biomedical Engineering Studio A OR 41160 Introduction to Biomedical Engineering) AND (120 credit points of completed study in Bachelor's Honours Embedded Degree owned by FEIT OR 120 credit points of completed study in Bachelor's Combined Honours Degree owned by FEIT OR 120 credit points of completed study in Bachelor's Combined Honours Degree co-owned by FEIT)) OR 42721 Introduction to Biomedical Engineering )
These requisites may not apply to students in certain courses. See access conditions.
Recommended studies:
basic knowledge of signal theory and basic skills of Matlab/Labview
Description
This subject covers the concepts of signal processing and modelling related to biomedical signals and images along with methods of acquisition and classification. Biomedical signals, such as, Electrocardiogram (ECG or EKG), surface Electromyogram (sEMG), human cardiorespiratory signals and body movement signals, are discussed in relation to discrete signal processing algorithms for the analysis and monitoring. For the signal analysis of human body movement (measured by portable inertial sensors), several well-known techniques such as the band-pass FIR filtering and Joint Time-Frequency Analysis (JTFA) (including Short-Term Fourier Transforms and Wavelet) are presented along with techniques for data classification. For the signal analysis of human cardiorespiratory system, K-mean clustering algorithms, Support Vector Machine (SVM), and most commonly used dynamic modelling approaches are also covered. Both stationary and non-stationary signal processing techniques for the analysis, detection and estimation of various cardiorespiratory signals are further discussed. Multidimensional filtering design for 2D image processing will also be explored.
Most of the discussed data processing techniques are demonstrated by using MATLAB simulation, tested in Labview environment, and implemented by using microcontroller and/or other specialised devices.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Apply advanced techniques for noise reduction and artifact removal for biomedical signals. (D.1) |
|---|---|
| 2. | Design, analyse and implement both analogue and digital filters to conform to given specifications. (C.1) |
| 3. | Develop modelling and feature extraction & classification skills for a number of biomedical signal applications. (D.1) |
| 4. | Acquire theoretical and practical skills by working in laboratories to build and test basic signal and image processing systems, and in a group project to further develop technical expertise, teamwork, research and communication skills. (D.1, E.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
- Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
- Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating autonomously within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
Contribution to the development of graduate attributes
Engineers Australia Stage 1 Competencies
Students enrolled in the Master of Professional Engineering should note that this subject contributes to the development of the following Engineers Australia Stage 1 competencies:
- 1.3. In-depth understanding of specialist bodies of knowledge within the engineering discipline.
- 1.4. 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.
Teaching and learning strategies
This subject is project oriented, with teamwork at its core. It allows students to develop their own solutions for complex biomedical signal processing problems. The assessment tasks are mainly practice-based and reflect current industry relevance. Face-to-face lectures are designed to facilitate student enquire, engagement and motivation to explore the exiting areas of biomedical signal processing.
Student learning is supported in the following way:
1. Prior to each lab, students are required to study the lecture notes and associated readings.
2. In the lab, students will work in groups on their project tasks.
3. Academic staff are available in each lab to review the work and provide informative feedback.
4. Students are permitted to enter the lab to carry out project work most of the time within the teaching period.
Content (topics)
The following topics will be covered:
• Description of the signal and image processing steps.
• Analogue and digital filter design.
• Signal/image enhancement through filtering and artefact removal.
• Spectral estimation, autoregressive and wavelet
• Joint Time-Frequency Analysis (JTFA) of biomedical signals.
• Feature extraction from signals/images.
• Image processing.
• Signal reconstruction techniques.
• Classification of biomedical signals/images.
• Analysis of certain types of cardiorespiratory signals.
Assessment
Assessment task 1: Lab Project 1: Evaluation of filter design for ECG signal processing
| Intent: | Demonstration of individual ability of analogue/digital Filter design for TA or ECG signals processing. Demonstration of ability to critically analyse the performance of the designed filters. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1, D.1 and E.1 |
| Type: | Project |
| Groupwork: | Group, individually assessed |
| Weight: | 25% |
Assessment task 2: Lab Project 2: Signal processing for non-stationary signals in Labview environment
| Intent: | Demonstration of individual ability of analysis of non-stationary signals by using Short-Term Fourier Transform. Demonstration of individual ability of analysis of non-stationary signals by using Wavelet. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1, D.1 and E.1 |
| Type: | Project |
| Groupwork: | Group, individually assessed |
| Weight: | 30% |
Assessment task 3: Lab Project 3: Signal/image processing and classification by using microcontroller
| Intent: | Demonstration of individual ability of design standalone systems (by using microcontroller) for the implementation of real-time signal/image processing. Demonstration of individual ability of design and implementation of signal classification algorithms. Demonstration of the ability of analysis of real time system performance of signal/image processing. Demonstration of the ability to communicate effectively with peers and general audiences. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1, D.1 and E.1 |
| Type: | Project |
| Groupwork: | Group, individually assessed |
| Weight: | 45% |
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Required texts
Robert B. Northrop, Signals and Systems Analysis In Biomedical Engineering, Second Edition, CRC Press, Taylor & Francis Group, LLC, Boca Raton, 2010
Recommended texts
1. Biomedical Signal And Image Processing by Kayvan Najarian, Robert Splinter, Taylor & Francis, (2006)
2. Digital signal processing system design: LabVIEW-based hybrid programming / By: Kehtarnavaz, Nasser. Published: (2008)
3. Biomedical Signal Processing and Signal Modeling Wiley Series In Telecommunications & Signal Processing, by Bruce Eugene N., John Wiley & Sons Inc (United States), 2001, ISBN-13:9780471345404, ISBN-10: 0471345407
4. Computational Intelligence in Biomedical Engineering by Rezaul Begg & Daniel T. H. Lai & Marimuthu Palanis, ISBN 9780849340802 - QBD
5. Practical Interfacing in the Laboratory: Using a PC for Instrumentation, Data Analysis and Control (Hardcover) by Stephen E. Derenzo, Cambridge University Press (2003) ISBN: 0521815274
6. Data Acquisition Techniques Using PCs, by Howard Austerlitz, Academic Press, Second Edition, 2002
7. Practical Data Acquisition for Instrumentation and Control Systems by John Park and Steve Mackay, Elsevier, 2003, ISBN: 07506 57960