Course coordinator
Dr Md Abdul Awal
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
Medical Signals: origins and characteristics; modelling medical signals and systems; interference, artefact and noise removal; waveform complexity and event detection; nonlinear methods in medical system identification; introduction to pattern classification and diagnostic decisions; emerging techniques in medical signal processing. Case studies on the use of signal processing methodologies in clinical instrumentation, imaging and medical decision making.
The purpose of this course is to introduce students to the fundamentals of biomedical signal processing, with particular emphasis on solving real-world problems in medical instrumentation design for clinical diagnosis. Through a series of focused active learning project activities using real-world signals, the course will provide opportunities to acquire in-depth knowledgeﾠof processing physiological data. Critical reflection on this active learning approach will allow students to creatively develop ways to enhance the signal processing techniques they learn about in this course. In addition, based on student feedback from previous offerings of the course, the assessments are now based on practical examples that require the application of learned concepts from the course. Finally, opportunities will be provided to acquire both independent and collaborative learning skills. Open-ended or partially solved problems will foster creativity.
Course requirements
Assumed background
Students are assumed to be competent in signals and systems at an intermediate level. Knowledge of a scientific programming language (e.g. Matlab) is strongly recommended.
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
(ELEC4403 or BIOE6403) or (ELEC4601 or BIOE6601), or ELEC4630, or ELEC7403, or ELEC7606
Recommended companion or co-requisite courses
We recommend completing the following courses at the same time:
ENGG7302 or (ENGG1001 or CSSE1001) or BIOE6901
Incompatible
You can't enrol in this course if you've already completed the following:
ELEC7902
Course contact
Course staff
Lecturer
Dr Md Abdul Awal
Dr Saskia Bollmann
Dr Steffen Bollmann
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
It is expected that upon successful completion of the courseﾠstudents will have an in-depth understanding of common biomedical signals, as well as the correspondingﾠdigital signal processing techniques that may be appliedﾠin biomedical engineering. In addition, students mayﾠbe able to select adequate methods for common problems in biomedical signal processing and implement the corresponding algorithms.ﾠFinally, for a selected set of state-of-the-art technologies, students should be able to understand the technical solution employedﾠand to discuss the approach based on an understanding of commonly applied methods.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Understand and describe properties of selected biomedical signals, including how these may affect related clinical applications.
LO2.
Identify appropriate solutions for specific problems in biomedical engineering by considering the benefits and limitations of various digital signal processing approaches.
LO3.
Implement signal processing algorithms for practical problems involving biomedical signals.
LO4.
Analyse selected physiological data with a particular focus on detecting events in biomedical signals.
LO5.
Demonstrate the need for digital signal processing in certain medical devices and clinical procedures.
LO6.
Propose, carry out, orally present, and report in conference-proceedings format, a research project on biomedical signal processing.
LO7.
Critically analyse information independently acquired from diverse sources (e.g., the Internet, books, electronic application notes, company product data sheets, databases such as the ISI Web of Science and IEEExplore) in providing solutions to real-world clinical biomedical signal analysis problems.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Paper/ Report/ Annotation |
Project 1
|
25% |
23/08/2024 4:00 pm |
| Computer Code, Paper/ Report/ Annotation, Tutorial/ Problem Set |
Project 2
|
25% |
20/09/2024 4:00 pm |
| Computer Code, Paper/ Report/ Annotation |
Project 3
|
25% |
18/10/2024 4:00 pm |
| Computer Code, Paper/ Report/ Annotation, Presentation, Reflection |
Conference paper about one of the 3 projects.
|
25% |
4/11/2024 4:00 pm |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Project 1
- Identity Verified
- Mode
- Oral, Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 25%
- Due date
23/08/2024 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07
Task description
The project regards the first module in the course (spectral analysis). The project involves developing computer code to analyse the given data and reproduce the results of the selected paper. Students formulate code, code is being run and checked, submit a short report, and then undergo a brief (e.g., 5–10 min) oral exam to demonstrate their code and verify that it is theirs and that they understand it. Students will sign up for a time from available sessions in the week immediately following the submission deadline to demonstrate their code. This will likely be outside of the normal scheduled class time.
Submission guidelines
Assignments are to be submitted online via Blackboard unless otherwise specified for a particular assessment item. The data, codes, and report will be in a single zip file with the student's name and student ID.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 14-21 days where the earlier time frame applies if no extensions.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Project 2
- Identity Verified
- Mode
- Oral, Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation, Tutorial/ Problem Set
- Weight
- 25%
- Due date
20/09/2024 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07
Task description
The project regards the second module in the course (event detection). The project involves developing computer code to analyse the given data and to reproduce the results of the selected paper. Students formulate code, code is being run and checked, submit a short report, and then undergo a brief (e.g., 5–10 min) oral exam to demonstrate their code and verify that it is theirs and that they understand it. Students will sign up for a time from available sessions in the week immediately following the submission deadline to demonstrate their code. This will likely be outside of the normal scheduled class time.
Submission guidelines
Assignments are to be submitted online via Blackboard unless otherwise specified for a particular assessment item. The data, codes, and report will be in a single zip file with the student's name and student ID.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 14-21 days where the earlier time frame applies if no extensions.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Project 3
- Identity Verified
- Mode
- Oral, Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 25%
- Due date
18/10/2024 4:00 pm
- Learning outcomes
- L01, L02, L05, L06, L07
Task description
The project regards the third module in the course (fMRI). The project involves developing computer code to analyse the given data and to reproduce the results of the selected paper. Students formulate code, code is being run and checked, submit a short report, and then undergo a brief (e.g., 5–10 min) oral exam to demonstrate their code and verify that it is theirs and that they understand it. Students will sign up for a time from available sessions in the week immediately following the submission deadline to demonstrate their code. This will likely be outside of the normal scheduled class time.
Submission guidelines
Assignments are to be submitted online via Blackboard unless otherwise specified for a particular assessment item. The data, codes, and report will be in a single zip file with the student's name and student ID.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 14-21 days where the earlier time frame applies if no extensions.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Conference paper about one of the 3 projects.
- Hurdle
- Identity Verified
- Mode
- Activity/ Performance, Oral, Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation, Presentation, Reflection
- Weight
- 25%
- Due date
4/11/2024 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07
Task description
Students will be required to write a 2-page conference paper about one of the three projects and seat for an oral exam (e.g., 5- 10 min). Students will sign up for a time from available sessions in the week immediately following the submission deadline to demonstrate their code. This will likely be outside of the normal scheduled class time. Students can choose which project they would like to use. The format of the project guidelines (template).
Hurdle requirements
Students will need to present and reflect their learning outcomes in question-and-answer (Q &A) session with lecturersSubmission guidelines
Assignments are to be submitted online via Blackboard unless otherwise specified for a particular assessment item.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 14-21 days where the earlier time frame applies if no extensions.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 20 - 44 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Your finalﾠmark will be determined by combining the marks from the various assessment components.
Overall marks are rounded to the nearest integer. Half marks will be rounded up.
Overall marks can be scaled up (but not scaled down) at the discretion of the course coordinator. ﾠ
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Use of AI Tools
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 13 |
Problem-based learning |
Projects Each main module of the course will feature an individual project where biomedical signal processing methods and real-world data are used. Learning outcomes: L01, L02, L03, L04, L05 |
Lecture |
Lectures Lecture on all topics addressed in the course. Learning outcomes: L01, L02, L03, L04, L05, L06, L07 |
Additional learning activity information
BIOE7902 is a course designed to introduce students to the fundamentals of biomedical signal processing. In addition to the lectures, the focus of the course is a series of active learning projects, providing practical experience to students in processing physiological data. The teaching of this course will be done in a combination of different instructional modes, as appropriate for each module and the class enrolment numbers. Contact hours will be mostly used for active learning project activities including student-driven computer laboratory-based work, while lectures will normally be employed conventional classroom lectures.
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: