CECS Professional Skills Mapping

ENGN4821 — Biomedical Signal Processing

code: ENGN4821
name: Biomedical Signal Processing
unit value: 6
description: This course covers the concepts behind analysing and processing signals and images of a biological system, including in 1D, 2D and 3D. Being able to interpret and respond to the variability of biomedical signals is key to many modern devices including ECG, pace-makers, life support systems, stress monitors, and cochlea and visual prostheses. The rapid processing of bio-signals and biomedical images is used in a wide range of diagnostic and interactive medical applications including wearable electrocardiograms, image guided surgery and disease diagnosis. This course introduces and explores concepts of analysing and processing images of biological systems using advanced mathematics. The course will examine the mathematical concepts of low-level signal and high dimensional image processing/analysis (e.g., enhancement and segmentation), and move to quantitative processing such as registration and diagnosis. The course will examine these in the context of a broad range of medical signals and images, such as heart monitors, ultrasound, CT, MRI and microscopy.
P&C: https://programsandcourses.anu.edu.au/course/ENGN4821
course learning outcomes:
  1. Understand how biosignals (after acquisition) are processed in one and higher dimensions.
  2. Demonstrate advanced knowledge of biomedical signal and image processing methods by being able to describe the theory and mathematics.
  3. Demonstrate an understanding signal representation and processing across a range of biomedical devices.
  4. Apply advanced knowledge in biomedical image processing to develop and implement biomedical algorithms for processing biomedical images and critically interpret their success.
  5. Understand what biomedical signals are, the different noise analysis methods, and how contrast can be enhanced using advanced mathematical methods.
  6. Demonstrate an understanding of the role of biomedical signal and image processing medical devices that is adaptable to a range of technologies.
assessment:
  1. End semester exam (50%)
  2. Research and Design Project (30%)
  3. Labs / mid term (20%)

Mapped learning outcomes

learning outcome1. KNOWLEDGE AND SKILL BASE2. ENGINEERING APPLICATION ABILITY3. PROFESSIONAL AND PERSONAL ATTRIBUTESassessment tasks
1.11.21.31.41.51.62.12.22.32.43.13.23.33.43.53.6123
  1. Understand how biosignals (after acquisition) are processed in one and higher dimensions.
  1. Demonstrate advanced knowledge of biomedical signal and image processing methods by being able to describe the theory and mathematics.
  1. Demonstrate an understanding signal representation and processing across a range of biomedical devices.
  1. Apply advanced knowledge in biomedical image processing to develop and implement biomedical algorithms for processing biomedical images and critically interpret their success.
  1. Understand what biomedical signals are, the different noise analysis methods, and how contrast can be enhanced using advanced mathematical methods.
  1. Demonstrate an understanding of the role of biomedical signal and image processing medical devices that is adaptable to a range of technologies.

Course contribution towards the Engineers Australia Stage 1 Competency Standard

This table depicts the relative contribution of this course towards the Engineers Australia Stage 1 Competency Standard. Note that this illustration is indicative only, and may not take into account any recent changes to the course. You are advised to review the official course page on P&C for current information..

1. KNOWLEDGE AND SKILL BASE
1.1
 
1.2
 
1.3
 
1.4
 
1.5
 
1.6
 
2. ENGINEERING APPLICATION ABILITY
2.1
 
2.2
 
2.3
 
2.4
3. PROFESSIONAL AND PERSONAL ATTRIBUTES
3.1
3.2
3.3
3.4
3.5
3.6

Engineers Australia Stage 1 Competency Standard — summary

1. KNOWLEDGE AND SKILL BASE
1.1Comprehensive, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline.
1.2Conceptual understanding of the, mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
1.3In depth understanding of specialist bodies of knowledge within the engineering discipline.
1.4Discernment of knowledge development and research directions within the engineering discipline.
1.5Knowledge of contextual factors impacting the engineering discipline.
1.6Understanding of the scope, principles, norms, accountabilities and bounds of contemporary engineering practice in the engineering discipline.
2. ENGINEERING APPLICATION ABILITY
2.1Application of established engineering methods to complex engineering problem solving.
2.2Fluent application of engineering techniques, tools and resources.
2.3Application of systematic engineering synthesis and design processes.
2.4Application of systematic approaches to the conduct and management of engineering projects.
3. PROFESSIONAL AND PERSONAL ATTRIBUTES
3.1Ethical conduct and professional accountability.
3.2Effective oral and written communication in professional and lay domains.
3.3Creative, innovative and pro-active demeanour.
3.4Professional use and management of information.
3.5Orderly management of self, and professional conduct.
3.6Effective team membership and team leadership.

Updated:  18 February 2021/ Responsible Officer:  Dean, CECS/ Page Contact:  CECS Academic Education Services