BMET9922: Computational Analysis for Biomedical Signals (2021 - Semester 2)

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Unit: BMET9922: Computational Analysis for Biomedical Signals (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Postgraduate
Faculty/School: School of Biomedical Engineering
Unit Coordinator/s: Dr Watkins, Greg
Session options: Semester 2
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Prohibitions: BMET2922.
Brief Handbook Description: 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.
Assumed Knowledge: Knowledge of basic biomedical engineering principles (BMET1960) and basic programming (ENGG1801 or ENGG1810 or ENGG9810 or INFO1110).
Lecturer/s: Dr Kumar, Ashnil
Timetable: BMET9922 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 1.00 1 13
2 Laboratory 3.00 1 11
3 Independent Study 5.00 1 13
T&L Activities: Lecture: Delivery of theoretical content

Laboratory: Practical computer labs

Independent study: Review of lecture & laboratory materials, and assessment tasks

Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.

Attribute Development Method Attribute Developed
Students are required to explain the mathematical theories behind biomedical signal analysis pipelines, both in laboratory exercises and in assessments. Based on this understanding, students will have to implement the appropriate software and hardware components to solve particular signal processing tasks. (1) Maths/ Science Methods and Tools (Level 2)
Students are given scenario(s) that require them to use various components and tools to create a pipeline to capture, transmit, analyse, and visualise biological signal data. Students have to implement an appropriate pipeline given the specific scenario. (2) Engineering/ IT Specialisation (Level 3)
In laboratory assignments and assessments, students are required to integrate hardware and software components to create solutions for remote patient monitoring tasks. (3) Problem Solving and Inventiveness (Level 2)
Students produce the design of a pipeline for biomedical signal analysis. They will describe the integration of various hardware and software components, identifying both strengths and limitations of their proposed design. (4) Design (Level 2)
Students are required to practice their written and oral communication skills through the assessments. They need to articulate the technical means through which their practical assessment meets the requirements of the scenario, and the justification for their design decisions. (6) Communication and Inquiry/ Research (Level 3)

For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table 2018.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

(6) Communication and Inquiry/ Research (Level 3)
1. Describe and document the process used to design and develop a pipeline to capture, process, and analyse biological signals using computational tools.
(4) Design (Level 2)
2. Produce a design plan for capturing and analysing biomedical signals
3. Identify and assess the strengths and limitations of a biomedical signal analysis pipeline
(2) Engineering/ IT Specialisation (Level 3)
4. Program solutions for biomedical signal processing tasks using existing software packages and libraries.
5. Integrate bioelectronic sensors with microcontrollers to capture biological signals
6. Apply computational tools to capture, store, transmit, analyse, and display biomedical signal data
(3) Problem Solving and Inventiveness (Level 2)
7. Apply theoretical and practical knowledge of biomedical signal analysis to implement the hardware and software components of a remote patient monitoring system
(1) Maths/ Science Methods and Tools (Level 2)
8. Explain the mathematical underpinnings of biomedical signal processing techniques
9. Apply signal and mathematical analysis methods to biomedical signal data
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Assignment: Design Proposal Yes 10.00 Week 5 1, 2, 3,
2 Mid-Semester Quiz No 10.00 Week 7 3, 7, 8, 9,
3 Assignment: System demonstration Yes 20.00 Week 12 3, 4, 5, 7, 9,
4 Assignment: System design report No 10.00 Week 13 1, 3, 7, 8,
5 Lab: Contribution No 10.00 Multiple Weeks 1, 3, 4, 5, 6, 7, 8, 9,
6 Written exam No 40.00 Exam Period 2, 3, 7, 8, 9,
Assessment Description: • Assignment: Design Proposal – students will propose a design for a system which collects data from a (supplied) biomedical sensor and displays the data in a simple graphical format on a host computer. The report will describe the system and software architecture, key system performance requirements, and provide a plan (schedule, risks, issues) for the implementation of the system.

• Quiz – a mid-semester quiz covering materials from lectures, tutorials, and laboratories.

• Assignment: System Demonstration – in a group, students will present the realisation of their design and justify the design choices that they have made.

• Assignment: Design Report – students will provide a brief report on the system operation and performance, as well as comparing the realisation of the system to the design proposal.

• Lab: Contribution – students will keep a logbook that details their contribution to each week's laboratory exercises and the lessons they have learned.

• Written exam: Final examination covering all materials in lectures, tutorials, laboratories, and assignments.
Assessment Feedback: The teaching team will provide feedback on the assessment tasks. Assignment results will be published on Canvas. Students are required to check their results. Any errors or omissions must be reported to the unit coordinator, with appropriate evidence, within 5 working days (a week) of being published; 5 days after being published, marks are considered to have been confirmed and will not subsequently be altered.

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp

Week Description
Week 1 Lecture: Introduction to the unit, learning outcomes, and assessments
Week 2 Lab: Introduction to software and tools to be used during the semester
Lecture: Biological signal data collection with microcontrollers
Week 3 Lab: Interfacing biomedical sensors with microcontrollers for simple I/O
Lecture: Biomedical sensors – Introduction to the types of sensor, application and key characteristics
Week 4 Lecture: Acquiring, storing and displaying signal data from biomedical sensors
Lab: Biosignal data acquisition and display
Week 5 Lecture: Processing biomedical signals
Lab: Capturing and processing signals simultaneously (multi-tasking)
Assessment Due: Assignment: Design Proposal
Week 6 Lab: Communicating signal data to a remote server (host)
Lecture: Data communication
Week 7 Lecture: Quiz
Lab: Project prototyping session
Assessment Due: Mid-Semester Quiz
Week 8 Lecture: Designing the host system for biomedical signal data
Lab: Remote monitoring of biomedical signal data (text)
Week 9 Lecture: Biosignal data processing and analysis on the host
Lab: Remote monitoring of biomedical signal data (graphical)
Week 10 Lab: System integration – data communication and remote control
Lecture: Putting it all together: System integration
Week 11 Lecture: Putting it all together: System validation
Lab: Project work
Week 12 Lab: System demonstration
Lecture: Case Study
Assessment Due: Assignment: System demonstration
Week 13 Lecture: Review – exam preparation
Assessment Due: Assignment: System design report
Exam Period Assessment Due: Written exam

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Master of Professional Engineering (Biomedical) 2021, 2018, 2019, 2020
Master of Engineering 2019, 2020, 2021
Master of Professional Engineering (Accelerated) (Biomedical) 2019, 2020

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
(6) Communication and Inquiry/ Research (Level 3) Yes 9%
(7) Project and Team Skills (Level 3) No 0%
(8) Professional Effectiveness and Ethical Conduct (Level 2) No 0%
(5) Interdisciplinary, Inclusiveness, Influence (Level 3) No 0%
(4) Design (Level 2) Yes 29.5%
(2) Engineering/ IT Specialisation (Level 3) Yes 14%
(3) Problem Solving and Inventiveness (Level 2) Yes 19.5%
(1) Maths/ Science Methods and Tools (Level 2) Yes 28%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.