Course coordinator
Dr Md Abdul Awal
Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- 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
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.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Paper/ Report/ Annotation, Performance |
Project 1
|
20% |
29/08/2025 4:00 pm |
| Computer Code, Paper/ Report/ Annotation, Tutorial/ Problem Set |
Project 2
|
20% |
26/09/2025 4:00 pm |
| Computer Code, Paper/ Report/ Annotation |
Project 3
|
20% |
24/10/2025 4:00 pm |
| Paper/ Report/ Annotation |
Conference paper about one of the 3 projects
|
40% |
10/11/2025 4:00 pm |
| Presentation |
Conference Paper (Oral Exam)
|
Pass/Fail |
Exam week 2 Mon - Exam week 2 Fri |
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
- Online
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation, Performance
- Weight
- 20%
- Due date
29/08/2025 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 topic or paper. Students formulate code, which is then run and checked, and assess the performance and robustness; a brief report is submitted. The data, codes, and report will be included in a single zip file, labelled with the student's name and student ID.
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.
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.
Extensions are limited to 7 days as feedback will be provided within 14 days.
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
- Online
- Mode
- Oral, Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation, Tutorial/ Problem Set
- Weight
- 20%
- Due date
26/09/2025 4:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07
Task description
The project regards the second module in the course. The project involves developing computer code to analyse the given data and to reproduce the results of the selected topic or paper. Students formulate code, which is then run and checked, and assess the performance and robustness; a brief report is submitted. The data, codes, and report will be included in a single zip file, labelled with the student's name and student ID.
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.
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.
Extensions are limited to 7 days as feedback will be provided within 14 days.
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
- Online
- Mode
- Oral, Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 20%
- Due date
24/10/2025 4:00 pm
- Learning outcomes
- L01, L02, L05, L06, L07
Task description
The project regards the third module in the course. The project involves developing computer code to analyse the given data and to reproduce the results of the selected topic or paper. Students formulate code, which is then run and checked, and assess the performance and robustness; a brief report is submitted. The data, codes, and report will be included in a single zip file, labelled with the student's name and student ID.
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.
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.
Extensions are limited to 7 days as feedback will be provided within 14 days.
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
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40%
- Due date
10/11/2025 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 sit an oral exam (refer to Conference Paper - Oral Exam assessment item).
- Students can choose any one of the above assignments they feel comfortable with.
- They must also submit the code they used in the conference paper and demonstrate it during the oral exam.
- A zip file containing data, code, and a conference paper is needed to submit.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
In order to pass the course, students must achieve at least a Pass (4) for the Conference Paper and the Oral Exam. Failure to meet this requirement will result in the final grade being capped at a 3, regardless of performance in other assessment items.Submission 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.
Extensions are limited to 7 days as feedback will be provided within 14 days.
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 (Oral Exam)
- Hurdle
- Identity Verified
- Mode
- Oral
- Category
- Presentation
- Weight
- Pass/Fail
- Due date
Exam week 2 Mon - Exam week 2 Fri
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07
Task description
This item forms the secure element of the Conference Paper task.
Students will be required to demonstrate the code they used in the conference paper during an oral exam.
Students will sign up for a time from available sessions in the week immediately following the submission deadline to demonstrate their work. The oral exam will likely be outside of the normal scheduled class time. The format of the project guidelines (template).
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
In order to pass the course, students must achieve at least a Pass (4) for the Conference Paper and the Oral Exam. Failure to meet this requirement will result in the final grade being capped at a 3, regardless of performance in other assessment items.Submission guidelines
Deferral or extension
You cannot defer or apply for an extension for this assessment.
If you are unable to attend your allocated oral exam, one reschedule is permitted. To arrange a reschedule, please contact your Course Coordinator directly.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
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 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 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
Having Troubles?
If you are having difficulties with any aspect of the course material, you should seek help and speak to the course teaching staff.
If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help; for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).
Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
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 |
Applied Class |
Projects Each main module of the course will feature an individual project where biomedical signal processing methods and real-world data are used. Informal feedback will be provided on a weekly basis during the weekly Practical and Applied Classes. Learning outcomes: L01, L02, L03, L04, L05 |
Lecture |
Lectures Lecture on all topics addressed in the course. Furthermore, informal feedback will be provided on a weekly basis during the applied class and practical sessions. Learning outcomes: L01, L02, L03, L04, L05, L06, L07 |
|
Practical |
Practicals There will be weekly practical sessions starting in week 1. The practical will be based on real-world signal processing. Gaining hands-on experience with real-world signals, including how to process them, remove noise, detect different abnormalities, and classify them. Learning outcomes: L01, L02, L03, L04, L05 |
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. Informal feedback will be provided on a weekly basis during the applied class and practical sessions.
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 for 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: