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Unit of study_

BMET2922: Computational Analysis for Biomedical Signals

Biomedical engineering is being deeply reshaped by the advancements in computational tools and the utilisation of rich data. This unit will explore the processes involved in designing and building systems to perform computational analysis on biological signals, using microcontrollers and desktop or server computing. The main teaching activities will focus on the theory and practical skills for data capture, cleaning, communication, storage, and analytics. The purpose is to ensure that students develop the skills necessary to design systems that can be used for monitoring of patients, where the data can be used for analytics, e.g. prediction of an adverse event. This is relevant to a number of applications in modern healthcare such as continuous and remote monitoring devices. The unit will develop core skills in programming, solution design, sensor interfacing, and data analysis.

Details

Academic unit Biomedical Engineering
Unit code BMET2922
Unit name Computational Analysis for Biomedical Signals
Session, year
? 
Semester 2, 2021
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Prohibitions
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BMET9922
Prerequisites
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None
Corequisites
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None
Assumed knowledge
? 

Knowledge of basic biomedical engineering principles (BMET1960) and basic programming (ENGG1801 or ENGG1810 or INFO1110).

Available to study abroad and exchange students

Yes

Teaching staff and contact details

Coordinator Greg Watkins, greg.watkins@sydney.edu.au
Type Description Weight Due Length
Final exam (Open book) Type C final exam hurdle task
45% Formal exam period 2 hours
Outcomes assessed: LO8 LO5 LO4 LO3 LO2 LO1
Small test Laboratory - knowledge assessment
0% Multiple weeks No weight in 2021
Outcomes assessed: LO1 LO7 LO5 LO4 LO3 LO2
Tutorial quiz hurdle task Quiz 0 - Expectation alignment
0% Week 01 15 minutes
Outcomes assessed: LO1
Assignment group assignment Major project - System Design Report
10% Week 05 n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Tutorial quiz Mid-semester quiz
10% Week 08 1 hour
Outcomes assessed: LO1 LO3 LO5 LO2 LO4 LO7
Presentation group assignment Major project - system demonstration
10% Week 12 20 minutes
Outcomes assessed: LO1 LO6 LO5 LO4 LO3 LO2
Assignment group assignment Major project - Project Report
25% Week 13 n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
hurdle task = hurdle task ?
Group assignment with individually assessed component = group assignment with individually assessed component ?
Type C final exam = Type C final exam ?
  • Quizzes: The first quiz must be completed in order to pass the unit of study. The mid-semester quiz may contain questions relating to any aspect of the unit up to and including the week prior to the quiz.
  • Assignments: Detailed information for each assessment can be found on Canvas.

Assessment criteria

 

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

 

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

The standard university policy regarding late submission will apply, unless noted otherwise on Canvas.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 Unit Overview and Python introduction Lecture and tutorial (2 hr) LO1 LO2
Week 02 Project requirements and planning, Python control flow and lists Lecture and tutorial (2 hr) LO1 LO2 LO3
Python introduction Practical (3 hr) LO1 LO3
Week 03 Micro-controller fundamentals, Python control flow, lists Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4
Microcontroller introduction Practical (3 hr) LO1 LO4
Week 04 Biomedical sensors, Python modules Lecture and tutorial (2 hr) LO1 LO3 LO4
Interfacing biomedical sensors Practical (3 hr) LO1 LO4
Week 05 Acquiring, storing and displaying signal data from biomedical sensors Lecture and tutorial (2 hr) LO3 LO4
Data Visualisation Practical (3 hr) LO1 LO3
Week 06 Designing the host system for biomedical signal data Lecture and tutorial (2 hr) LO1 LO3 LO5
Bio signal data acquisition and display Practical (3 hr) LO1 LO3 LO4
Week 07 Data communication Lecture and tutorial (2 hr) LO1 LO3 LO4 LO5
System design and test Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 08 Mid-semester quiz Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4 LO5 LO7
System design and test Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 09 Putting it all together: System integration Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
System design and test Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 10 Putting it all together: System validation Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
System design and test Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 11 Putting it all together: System validation Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
System design and test Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 12 Case Study Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
System demonstration Practical (3 hr) LO6
Week 13 Course review Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4

Attendance and class requirements

Opportunities for discussion of course concepts are provided in lectures and labs. Students are expected to attend a minimum of 90% of all timetabled activities.

See the Faculty resolutions for more information: https://www.sydney.edu.au/handbooks/engineering/rules/faculty_resolutions.shtml

Attendance in labs (practical sessions) is required and will be assessed.

 

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Understand and communicate the principles of operation of computational systems in a biomedical device context.
  • LO2. Produce a design plan for capturing and analysing biomedical signals
  • LO3. Program solutions for biomedical signal processing tasks using existing software packages and libraries.
  • LO4. Integrate bio-electronic sensors with micro-controllers to capture biological signals
  • LO5. Apply computational tools to capture, store, transmit, analyse, and display biomedical signal data
  • LO6. Work together in small groups to carry out a prescribed engineering design task and present the outcomes in an oral, written or video format
  • LO7. Apply engineering principles to answer questions relating to biomedical software systems in a quiz format.
  • LO8. Apply engineering principles to answer questions relating to biomedical software systems in an exam format.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
This is the initial presentation of this course.

The course assumes knowledge of “c” and python. In 2021 additional lecture and lab content has been added to provide python knowledge. From 2022 python knowledge will be assumed from ENGG1810. “c” and some micrcontroller knowledge is assumed from BMET1960.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.