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SUBJECT OUTLINE 42906 Biomedical Signal Processing Course area

UTS: Engineering

Delivery

Autumn 2018; City

Subject Field of practice: Biomedical Engineering major classification Credit points 6cp Requisite(s)

(48541 Signal Theory OR 48540 Signals and Systems OR 41090 Information and Signals) AND (120 Credit Points in spk(s): C10061 Bachelor of Engineering Diploma in Engineering Practice OR 120 Credit Points in spk(s): C10067 Bachelor of Engineering OR 120 Credit Points in spk(s): C10066 Bachelor of Engineering Science OR 120 Credit Points in spk(s): C09067 Bachelor of Engineering (Honours) Diploma in Professional Engineering Practice OR 120 Credit Points in spk(s): C09066 Bachelor of Engineering (Honours)) These requisites may not apply to students in certain courses. See access conditions.

Result type

Grade and marks

Attendance: 4hpw Recommended studies: Basic knowledge about signal theory and basic skills of Matlab/Labview.

Subject coordinator Associate Professor Steven Su Email: [email protected] Room: CB11.09.105 Phone +61 2 9514 7603 Contact by email is preferred.

Teaching staff Associate Professor Steven Su Email: [email protected] Room: CB11.09.105 Phone +61 2 9514 7603 Contact by email is preferred. Dr Ahmed Al-Ani Email: [email protected] Room: CB11.09.117 Phone: +61 2 9514 2420 Mr Jan Szymanski Email: [email protected]

Subject description This subject covers the concept of signal processing and modelling related to biomedical signals and images along with methods of acquisition and classification. Basics of some commonly encountered biomedical signals, such as human cardiorespiratory signals and body movement signals, are discussed along with discrete signal processing algorithms for the analysis and monitoring. For the analysis of human body movement signals (measured by portable inertial sensors), some well-known techniques such as the band pass digital filtering and the Joint Time-Frequency Analysis (JTFA) (including Short-Term Fourier Transforms and Wavelet) are included along with techniques for data classification. For the analysis of human cardiorespiratory signals, the K-mean clustering algorithms, Support Vector Machine, and most commonly used dynamic modelling approaches are also covered. Both stationary and 05/03/2018 (Autumn 2018)

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non-stationary signal processing techniques for the analysis, detection and estimation of various cardiorespiratory signals are included. Multidimensional filtering design for 2D image processing is discussed. Most of the discussed data processing techniques are demonstrated by using MATLAB simulation, tested in Labview environment, and implemented by using microcontroller and specialised devices.

Subject learning objectives (SLOs) Upon successful completion of this subject students should be able to: 1. Apply the general procedure for processing biomedical signals and images. 2. Develop algorithms for noise and artefact removal. 3. Analyse and detect certain patterns in the processed signals/images. 4. Analyse, design, and implement both analogue and digital filters to conform to given specifications. 5. Develop feature extract and classification skills for a number of biomedical signal applications. 6. Acquire theoretical and practical skills by working in the laboratories to build and test basic signal and image processing systems, and in a group project to further develop technical expertise, teamwork, research and communication skills

Course intended learning outcomes (CILOs) This subject also contributes specifically to the development of the following faculty Course Intended Learning Outcomes (CILOs) and Engineers Australia (EA) Stage 1 competencies: Apply systems thinking to understand complex system behaviour including interactions between components and with other systems (social, cultural, legislative, environmental, business etc.) (A.5) Synthesise alternative/innovative solutions, concepts and procedures (B.3) Apply decision-making methodologies to evaluate solutions for efficiency, effectiveness and sustainability (B.4) Implement and test solutions (B.5) Develop models using appropriate tools such as computer software, laboratory equipment and other devices (C.2) Reflect on personal and professional experiences to engage in independent development beyond formal education for lifelong learning (D.2) Identify and apply relevant project management methodologies (E.3)

Contribution to the development of graduate attributes Engineers Australia Stage 1 competencies Students enrolled in the Master of Professional Engineering Practice should note that this subject contributes to the assurance of Engineers Australia Stage 1 competencies: 1.3, 1.4, 2.2, 2.3, 2.4, 3.2, 3.3, 3.6.

Teaching and learning strategies This subject is Project/Practice oriented, with team work. It allows students to develop their own solutions for complex biomedical signal processing problems. Except the final exam, most assessment tasks are practice-based and reflect current industry practice. Face-to-face lectures are also designed to facilitate students to finish project tasks on time. Student learning is supported in the following way: 1. Prior to each lab, students are required to study the lecture notes and associated readings. 2. In the lab, students will work in groups on their project tasks. 3. Academic staff are available in each lab to review the work and provide immediate feedback. 4. Students are permitted to enter the lab to carry out project work most of the time within the teaching period.

Content (topics) The following topics will be covered: • Description of the signal and image processing steps. • Analogue and digital filter design. • Signal/image enhancement through filtering and artefact removal. 05/03/2018 (Autumn 2018)

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• Spectral estimation, autoregressive and wavelet • Joint Time-Frequency Analysis (JTFA) of biomedical signals. • Feature extraction from signals/images. • Image processing. • Signal reconstruction techniques. • Classification of biomedical signals/images. • Analysis of certain types of cardiorespiratory signals.

Program Week/Session

Dates

Description

OW

5 Mar

Review basic knowledge of signals and systems, e.g., first and second order linear time invariant systems. Learn Matlab and Labview from website. Please create a NI User Profile using your UTS Email ID (@uts.edu.au) as this will be required to access the Self-Paced Online Training using the following link: ni.com/self-paced-training . Please go through the contents of the LabVIEW Core-1 Training Module Notes: Read materials of Lecture 1&2 in UTSonline

1

12 Mar

Introduction of subject (subject content + subject structure) and reviewing of signals systems. Lab & project: Orientation (grouping).

2

19 Mar

Lecture: Fourier transform and analogue filter design. Project stage 1: Design of filters for Accelerometer/ECG processing.

3

26 Mar

DFFT and digital filter design. Lab & project: a. Design an analogue filter for Accelerometer or ECG signal processing.

4

2 Apr

Joint Time-Frequency Analysis (JTFA) of biomedical signals (Part I. Short-Term Fourier Transforms). Lab & project: b. Design a digital filter by using Matlab and implement in Labview environment.

5

9 Apr

Joint Time-Frequency Analysis (JTFA) of biomedical signals (Part II. Wavelet). Tutorial: Tutorial for Short-Term Fourier Transforms. Lab & project: Assessment of project stage 1. Demonstration of ability of analogue/digital filter design for IMU signals processing. Demonstration of ability to critically analyse the performance of the designed filters.

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6

16 Apr

Discrete wavelet transform (DWT) by using Matlab. Lab & project: Project stage 2 Signal processing for non-stationary signals in Matlab/ Labview environment. a. Short-Term Fourier Transform for IMU signal during walking, running, and the transition in between.

StuVac1

23 Apr

7

30 Apr

Analysis of portable sensor signals (heart rate (Polar HR monitor), body movement (IMU), breath rate, and oxygen consumption (K4b2)). Lab & project: b.Wavelet Analysis for Triaxial Accelerometer/ECG/EMG signals.

8

7 May

Modelling of cardio-respiratory response to exercise (Part I, Static Nonlinear Model & Part II, Dynamic Linear Model). Lab & project: Assessment of project stage 2. Demonstration of individual ability of analysis of non-stationary signals by using Short-Term Fourier Transform. Demonstration of individual ability of analysis of non-stationary signals by using Wavelet. Demonstrate the ability of the implementation using Labview.

9

14 May

Introduction of practical classification methods (e.g., Support Vector Machine). Lab & project: Project stage 3: Signal processing and classification by using microcontroller/ NI ELVIS phototype board. a. Algorithm design.

10

21 May

Multi-dimensional filter design and image processing of X-ray Lab & project: b. Hardware and software design and implementation.

11

28 May

Image processing for skin cancer detection and/or motion detection Lab & project: c. System performance tests.

12

4 Jun

Final Review. Lab & project: Seminar Oral presentation of final project. Assessment of project stage 3. Demonstration of individual ability of design standalone systems for the

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implementation for real-time signal/image processing.

Additional information Repeated Failure in this Subject The Faculty takes repeated failures in a subject seriously and enforces Rule 10.6 of the University’s Student and Related Rules. You should read these rules and be aware of the consequences of failure. If you have failed twice before in this subject, then: (i) You must seek advice from the Subject Coordinator. You will be asked to draw up and submit a study plan that outlines your strategy for passing this subject on the third attempt. A signed copy of this study plan will be kept by the Faculty for internal records. (ii) If you do not seek advice from the Subject Coordinator by Week 2, then you do not have the Faculty’s permission to enrol in the subject. If you stay enrolled in the subject then you will be breaking Rule 10.6.2 (1) of the University’s Student and Related Rules. (iii) You need to be aware that if you fail this subject for a third time, you will need to seek permission from the Deputy Head of School (Teaching & Learning) for any further enrolment in this subject (see below). If you fail this subject for a third time, then: (i) The Subject Coordinator will deny permission for any further enrolment unless you can produce documentary evidence of extenuating circumstances that require special consideration. In such cases, the Subject Coordinator will refer the matter to the Deputy Head of School (Teaching & Learning), who will grant or deny enrolment for a fourth or subsequent attempt based on a student’s overall performance in the course and the extent to which extenuating circumstances have contributed to one or more of the failures. (ii) If you are granted permission for a fourth or subsequent attempt at this subject, then you must seek continuing assistance throughout this semester from the Subject Coordinator.

Assessment Late Submission of Assessment Tasks Unless otherwise specified, late submission of an assessment task will attract a 20% penalty per working day, up to a maximum of 5 working days. If late submission of an assessment item is due to extenuating or special circumstances beyond your control, then you should contact the Subject Coordinator.

Assessment task 1: Lab project 1: Test of filter design for ECG signal processing Intent:

Demonstration of individual ability of analogue/digital Filter design for TA or ECG signals processing. Demonstration of ability to critically analyse the performance of the designed filters.

Objective(s): This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3, 4 and 6 This assessment task contributes to the development of the following course intended learning outcomes (CILOs): A.5, B.3, B.4, B.5 and C.2 Type:

Project

Groupwork:

Group, group assessed

Weight:

15%

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Task:

Design of filters for ECG signal processing: a. Design an analogue filter for an ECG signal generator. b. Design a digital filter by using Matlab and implement in Labview environment.

Due:

Week 5

Criteria linkages:

Criteria

Weight (%)

SLOs

CILOs

Demonstration of individual ability of analogue/digital Filter design for TA or ECG signals processing

50

1, 2, 3, 4, 6

A.5, B.3, B.4, B.5, C.2

Demonstration of ability to critically analyse the performance of the designed filters.

50

1, 2, 3, 4, 6

A.5, B.3, B.4, B.5, C.2

SLOs: subject learning objectives CILOs: course intended learning outcomes

Assessment task 2: Lab project 2: Signal processing for non-stationary signals in Labview environment Intent:

Demonstration of individual ability of analysis of non-stationary signals by using Short-Term Fourier Transform. Demonstration of individual ability of analysis of non-stationary signals by using Wavelet.

Objective(s): This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 5 and 6 This assessment task contributes to the development of the following course intended learning outcomes (CILOs): A.5, B.3, B.5 and E.3 Type:

Project

Groupwork:

Group, individually assessed

Weight:

20%

Task:

Lab project 2: Signal processing for non-stationary signals in Labview environment.

Due:

Week 8

Criteria linkages:

Criteria

Weight (%)

SLOs

CILOs

Demonstration of individual ability of analysis of non-stationary signals by using Short-Term Fourier Transform.

50

1, 2, 5, 6

A.5, B.3, B.5, E.3

Demonstration of individual ability of analysis of non-stationary signals by using Wavelet.

50

1, 2, 5, 6

A.5, B.3, B.5, E.3

SLOs: subject learning objectives CILOs: course intended learning outcomes

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Assessment task 3: Lab project 3: Signal/image processing and classification by using microcontroller Intent:

Demonstration of individual ability of design standalone systems (by using microcontroller) for the implementation of real-time signal/image processing. Demonstration of individual ability of design and implementation of signal classification algorithms. Demonstration of the ability of analysis of real time system performance of signal/image processing. Demonstration of the ability to communicate effectively with peers and general audiences.

Objective(s): This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3, 4, 5 and 6 This assessment task contributes to the development of the following course intended learning outcomes (CILOs): A.5, B.3, B.5, D.2 and E.3 Type:

Project

Groupwork: Group, group assessed Weight:

25%

Task:

There are possibly several different kinds of projects for students to select: audio signal processing, image processing, and signal processing for portable wireless sensors. After selecting a project, the students are asked to design a standalone signal/image processing and classification system by using microcontroller, which includes: a. Design of signal/image processing and classification algorithms. b. Design and implement of hardware and software for the standalone system. c. Test of system performance of the real time standalone system. d. Perform an oral presentation for the demonstration of project work.

Due:

Week 13

Criteria linkages:

Criteria

Weight (%)

SLOs

CILOs

Demonstration of individual ability of design standalone systems (by using microcontroller) for the implementation of real-time signal/image processing.

25

5, 6

B.5, E.3

Demonstration of individual ability of design and implementation of signal classification algorithms.

25

2, 3, 6

B.3, E.3

Demonstration of the ability of analysis of real time system performance of signal/image processing.

25

1, 3, 4, 5, 6

A.5, B.3, D.2, E.3

Demonstration of the ability to communicate effectively with peers and general audiences.

25

6

D.2

SLOs: subject learning objectives CILOs: course intended learning outcomes

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Assessment task 4: Final exam (restricted open book) Intent:

To test students’ understanding of key concepts and subject-specific skills. To evaluate the depth of students’ understanding of the fundamental knowledge of signal/image processing for typical biomedical systems.

Objective(s): This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3, 4, 5 and 6 This assessment task contributes to the development of the following course intended learning outcomes (CILOs): B.3, B.5, D.2 and E.3 Type:

Examination

Groupwork:

Individual

Weight:

40%

Task:

Final exam (restricted open book)

Due:

UTS Exam period

Criteria linkages:

Criteria

Weight (%)

SLOs

CILOs

To test students’ understanding of key concepts and subject-specific skills.

50

1, 2, 3, 4

B.3, B.5, D.2

To evaluate the depth of students’ understanding of the fundamental knowledge of signal/image processing for typical biomedical systems.

50

5, 6

E.3

SLOs: subject learning objectives CILOs: course intended learning outcomes

Further information:

Students are able to bring one A4 page, double sided handwritten summary notes.

Assessment feedback Include both formative and summative feedback.

Minimum requirements In order to pass the subject, you must: earn at least 15% for assessment task 4, final examination AND earn an overall total of 50 marks or more in the subject. Students who do not meet the minimum project-related requirements but achieve an overall mark of 50% or greater will fail the subject and receive their overall mark with an "X" g...