ELEC7902 - Sem 2 2008 - St Lucia - Internal

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Printed: 13 June 2008, 01:20PM
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1. General Course Information

1.1 Course Details

Course Code: ELEC7902 Course Title: Clinical Biomedical Signal Processing
Coordinating Unit: School of Information Technology and Electrical Engineering
Semester: Semester 2, 2008    Mode: Internal
Level: Postgraduate Coursework
Location: St Lucia
Number of Units: 2    Contact Hours Per Week: 3L2C
Recommended Pre-Requisites: ELEC7403 or ELEC7601 or ELEC7606
Recommended Companions: ENGG7302
Course Description: 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.
Assumed Background:

ELEC7902 is available to students enrolled in the Master of Engineering suite of programs. Undergraduate students who are interested in taking the course should contact the course-coordinator. The assumed knowledge for the subject is the content equivalents of ELEC7403 (Biomedical Instrumentation), ELEC3600/7601 (Digital Signal & Image Processing I) and prerequisites required for those subjects; probability and statistics (equivalent of STAT2202).

1.2 Course Introduction

The purpose of this course is to introduce students to the important aspects of signal processing as applicable in medical instrument design and clinical diagnosis. ELEC7902 will provide opportunities to acquire in-depth knowledge in important aspects of the practice of Biomedical Signal processing, through a series of focused project activities on real-world signals. Opportunities will be provided to acquire independent learning skills via directed reading of subject matter. Creativity will be fostered by attempting (guided) solutions to open-ended or partially solved problems

1.3 Course Staff

Course Coordinator: Dr Udantha Abeyratne
Phone: 3346 9063     Email: udantha@itee.uq.edu.au
Campus: St Lucia Building: General Purpose South (Map)   Room: 546
Consultation:

Consultation hours to be announced.


LECTURERS
    Dr. Udantha Abeyratne
    Dr. Andrew Bradley
    Depending on student enrollment numbers guest lecturers from the industry/hospitals may be invited to participate in teaching the course.


1.4 Timetable

Timetables are available on mySI-net.

2. Aims, Objectives & Graduate Attributes

2.1 Course Aims

The purpose of this course is to introduce students to the important aspects of signal processing as applicable in medical instrument design and clinical diagnosis.

2.2 Learning Objectives

After successfully completing this course you should be able to:

1  identify models of learning as applicable to your university and life-long education, with particular relevance to a multidisciplinary subject such as Biomedical Signal Processing.
2  understand the uses and importance of signal processing in medical instrument design and clinical practice.
3  understand the processes of sampling, quantization and filtering of clinical biomedical signals, and, elements of Stochastic Signal processing; uses in Auditory Brainstem Response analysis.
4  understand the acquisition, uses and linear & nonlinear processing of clinical signals obtained in an overnight polysomnography (PSG).
5  understand: some physiological correlates of "relaxation" and measure them within the laboratory; uses of music therapy in medicine; digital filter design and linear signal processing; experiment design.
6  Seek information independently from diverse sources (eg: the Internet, books, electronic application notes, company product data sheets, databases such as the ISI Web of Science and IEEExplore) in providing solutions to a real-world biomedical signal analysis problems.
7  apply some standard techniques of event detection and waveform shape/complexity analysis in biomedical signal processing problems.

2.3. Graduate Attributes

Successfully completing this course will contribute to the recognition of your attainment of the following UQ (Postgrad Coursework) graduate attributes:

GRADUATE ATTRIBUTELEARNING OBJECTIVES
A. IN-DEPTH KNOWLEDGE OF THE FIELD OF STUDY
A2. A broad understanding of the field of study, including how other disciplines relate to the field of study. 1, 2, 4, 5, 6
A3. A comprehensive and in-depth knowledge in the field of study.3, 5, 7
A5. An international perspective on the field of study. 
A7. An appreciation of the link between theory and practice. 
B. EFFECTIVE COMMUNICATION
B1. The ability to collect, analyse and organise information and ideas and to convey those ideas clearly and fluently, in both written and spoken forms.1, 3, 6
B2. The ability to interact effectively with others in order to work towards a common outcome. 
B3. The ability to select and use the appropriate level, style and means of communication. 
B4. The ability to engage effectively and appropriately with information and communication technologies. 
B5. The ability to practise as part of an interdisciplinary team.1, 4
C. INDEPENDENCE AND CREATIVITY
C2. The ability to work and learn independently and effectively.1, 6, 7
C3. The ability to generate ideas and adapt innovatively to changing environments. 
C5. The ability to formulate and investigate problems, create solutions, innovate and improve current practices.3, 5, 6, 7
C6. The abilities and skills that provide a foundation for future leadership roles.1
D. CRITICAL JUDGEMENT
D2. The ability to apply critical reasoning to issues through independent thought and informed judgement.5, 7
D4. The ability to process material and to critically analyse and integrate information from a wide range of sources.3, 4, 5, 6
D5. The ability to evaluate opinions, make decisions and to reflect critically on the justifications for decisions using an evidence-based approach.5
E. ETHICAL AND SOCIAL UNDERSTANDING
E1. An understanding of social and civic responsibility. 
E3. An appreciation of the philosophical and social contexts of a discipline.4
E4. A knowledge and respect of ethics and ethical standards in relation to a major area of study. 
E5. A knowledge of other cultures and times and an appreciation of cultural diversity. 
E7. The ability to work effectively and sensitively across all areas of society. 
E8. An understanding of and respect for the roles and expertise of associated disciplines. 

3. Learning Resources

3.2 Recommended Resources

Eugene N. Bruce, Biomedical Signal Processing and Signal Modeling,  John Wiley & Sons, New York, 2001  
 

J.G.Webster, Medical Instrumentation Application and Design, 3rd Ed., John Wiley & Sons, New York, USA.

 
 
A. Papoulis, Probability, Random Variables and Stochastic Processes, McGraw-Hill Inc., New York, USA.  
 
S.K.Mitra, Digital Signal Processing- A Computer-based Approach, McGraw-Hill, New York, USA.  
 

BIOMEDICAL Signal Processing and Signal Modeling [copy]

URL
 
EEG-BASED brain-computer interfaces URL
 
Neural Networks [for nonlinear signal modelling] URL
 
DIGITAL filter design URL
 
Course Guides / Notes/ Copies of Slides as appropriate, if/when provided by individual lectures.  
 
MULGREW B GRANT P and THOMPSON J, "DIGITAL SIGNAL PROCESSING: CONCENPTS AND APPLICATIONS," 2ND ED, Palgrave Macmillan, 2003. URL
 
Pattern Classification, R.O.Duda, P.E. Hart and D.G.Stork, 2nd Ed, Wiley Interscience, ISBN:0-471-05669-3  
 

3.3 University Learning Resources

Access to required and recommended resources, plus past central exam papers, is available at the UQ Library website (http://library.uq.edu.au/search/r?SEARCH=ELEC7902).

The University offers a range of resources and services to support student learning. Details are available on the myServices website (https://student.my.uq.edu.au/).

3.4 School of Information Technology and Electrical Engineering Learning Resources

Students enrolled at St Lucia who wish to retain a hard copy of this profile can use the free print quota provided each semester to students enrolled in courses in the School of Information Technology & Electrical Engineering. For information on how to use this print quota, see the School Policy on Student Photocopying and Printing (St Lucia) (http://www.itee.uq.edu.au/about_ITEE/policies/copy-print.html). Students enrolled at the Ipswich campus will either be provided with a hard copy or given directions in class on how to obtain a free copy.

ITEE course websites can be found at http://www.itee.uq.edu.au/~COURSECODE. Many ITEE courses also have Usenet newsgroups, named uq.itee.COURSECODE. Instructions for accessing newsgroups are available at http://studenthelp.itee.uq.edu.au/faq/1stYearFAQ.html#accessnews.

4. Teaching & Learning Activities

4.1 Learning Activities

Date
Activity
Learning Objectives
21 Jul 08 - 21 Jul 08
Introduction & Learning How to Learn (Discussion): [Module-1] Introduction to ELEC7902: How to learn Biomedical Signal Processing. The transmission mode vs. the active mode; superficial learning vs. deep learning. Introduction to modes of learning in ELEC7902.

Readings/Ref: Class_Notes (Module-1 Course Guide, provided via Blackboard.MUST READ BEFORE COMING TO CLASS.)
1, 6
23 Jul 08 - 11 Aug 08
The Auditory Brainstem Response (ABR) (Module-2): Physiology of hearing; brainstem anatomy; ABR acquisition; time-locked averaging; temporal and spectral properties of the ABR. Sampling and Quantisation: Stochastic Signal Processing:
Readings/Ref: Papoulis ; Class_Notes ; Mulgrew
2, 3, 6, 7
13 Aug 08 - 1 Sep 08
Polysomnography Signals [Module 2] (Lectures, discussion & computer labs): Polysomonography signals: clinical uses, acquisition and processing. Linear/nonlinear modelling with application to artefact detection, signal prediction and event detection.
Readings/Ref: Eugene_online ; ANN ; Class_Notes ; duda
2, 4, 6, 7
3 Sep 08 - 22 Sep 08
Music and Physiological Signals [Module 4] (Project based Learning): Music, Sports and Physiological Signals (The Signal Processing Challenge). Experiment design, signal acquisition, digital filtering and EEG decomposition.
Readings/Ref: Class_Notes ; Brain_EEG ; Mitra ; Webster ; Mitra-Filter ; duda
2, 3, 5, 6, 7
24 Sep 08 - 20 Oct 08
Introduction to Adaptive Filtering [Module 5] (Case Study): In this module, students will study elements of Adptive Signal processing techniques and applications in removing unwanted interference from medical signals.
Readings/Ref: Eugene_online ; Class_Notes ; Mitra ; Mitra-Filter ; duda
2, 3, 6, 7

4.2 Other Teaching and Learning Activities Information

ELEC7902  is a specialized course, designed with students who are interested in Biomedical Signal Processing into account.  The subject will be taught through a number of individual learning modules. The teaching of this subject will be done in a combination of different instructional modes, as appropriate for each module and the class enrolment numbers. The lecture hours may be used for traditional classroom lectures and/or for hands on biomedical signal acquisition work of the course. The other contact hours may be used for Active Learning activities including computer laboratory-based work.

5. Assessment

5.1 Assessment Summary

This is a summary of the assessment in the course. For detailed information on each assessment, see 5.5 Assessment Detail below.

Assessment Task
Due Date
Weighting
Learning Objectives
DEMO-1
Module 2: Lab. Demonstration plus Presentation
11 Aug 08
To be finalized with student consultation.
15%
2, 3, 6
DEMO-2
Module 3: Lab. Demonstration plus Presentation
1 Sep 08
To be finalized with student consultations.
15%
2, 3
DEMO-3
Module 4: Lab. Demonstration plus Presentation
22 Sep 08
To be finalized with student consultations.
15%
1, 2, 3, 4
DEMO-4
Module 5: Lab. Demonstration plus Presentation
20 Oct 08
To be finalized with student consultations.
15%
2, 3, 4
Exam - during Exam Period (School)
Final Exam
Examination Period
40%
2, 3, 4, 5, 7

5.2 Course Grading


Grade 1, Fail: Fails to demonstrate most or all of the basic requirements of the course:

Grade 1 will be given for an overall course mark of 19 or lower.




Grade 2, Fail: Demonstrates clear deficiencies in understanding and applying fundamental concepts; communicates information or ideas in ways that are frequently incomplete or confusing and give little attention to the conventions of the discipline:

A grade of 2 requires earning an overall course mark bewteen 20 and 44.




Grade 3, Fail: Demonstrates superficial or partial or faulty understanding of the fundamental concepts of the field of study and limited ability to apply these concepts; presents undeveloped or inappropriate or unsupported arguments; communicates information or ideas with lack of clarity and inconsistent adherence to the conventions of the discipline:

A grade of 3 requires earning an overall course mark between 45 and 49.




Grade 4, Pass: Demonstrates adequate understanding and application of the fundamental concepts of the field of study; develops routine arguments or decisions and provides acceptable justification; communicates information and ideas adequately in terms of the conventions of the discipline:

A grade of 4 requires earning an overall course mark between 50 and 64.




Grade 5, Credit: Demonstrates substantial understanding of fundamental concepts of the field of study and ability to apply these concepts in a variety of contexts; develops or adapts convincing arguments and provides coherent justification; communicates information and ideas clearly and fluently in terms of the conventions of the discipline:

A grade of 5 requires earning an overall course mark between 65 and 74.




Grade 6, Distinction: As for 5, with frequent evidence of originality in defining and analysing issues or problems and in creating solutions; uses a level, style and means of communication appropriate to the discipline and the audience:

A grade of 6 requires earning an overall course mark between 75 and 84.

.



Grade 7, High Distinction: As for 6, with consistent evidence of substantial originality and insight in identifying, generating and communicating competing arguments, perspectives or problem solving approaches; critically evaluates problems, their solutions and implications:

A grade of 7 requires earning an overall course mark of 85 or higher.


5.3 Late Submission

No extensions will be granted except in exceptional personal circumstances (documented medical reason or family emergency). Personal hardware or computer failures are not grounds for extension.

5.4 Other Assessment Information

Your final mark will be determined by combining the marks from the various assessment components as described in Section 5.1 (Assessment Summary).

Your final grade (1-7) will be determined according to the following table:

Final Mark                            Grade

85-100                                     7

75- 84                                      6

65-74                                       5

50-64                                       4

45-49                                       3

20-44                                       2

00-19                                       1

5.5 Assessment Detail


Module 2: Lab. Demonstration plus Presentation
Type: DEMO-1
Learning Objectives Assessed: 2, 3, 6
Due Date:
         11 Aug 08     To be finalized with student consultation.
Weight: 15%
Task Description:

Students will be required to make a 30 minute  presentation and answer questions on the outcomes of  laboratory work. In particular students should present all of the results from the laboratory sessions and describe in detail how the algorithms are implemented in Matlab. Visual aids (PowerPoint) are highly recommended.

Depending on the class size, you may be required to make an individual or group presentation. The exact date of this assessment item will be determined by individual lecturers in consultation with the class.


Criteria & Marking:

Marks will be assigned as follows:

Content (60%): Including: Introduction and background theory, aims, methodology, results, conclusions and ANSWERS TO ALL OF THE QUESTIONS IN THE PROJECT OUTLINE
Presentation (20%): Logical flow of the ideas presented in seminar and between the individual presenters; presenters should be confident; appropriate use of visual aids and diagrams to support the presentation. Visual aids need to be clear and uncluttered, with an appropriate level of detail and overall number of slides ; 
Question & Answer (20%): Questions are answered convincingly and honestly, i.e., answering “I don’t know” is better than pretending you do



Module 3: Lab. Demonstration plus Presentation
Type: DEMO-2
Learning Objectives Assessed: 2, 3
Due Date:
         1 Sep 08     To be finalized with student consultations.
Weight: 15%
Task Description:

Students will be required to make a 30 minute  presentation and answer questions on the outcomes of  laboratory work. In particular students should present all of the results from the laboratory sessions and describe in detail how the algorithms are implemented in Matlab. Visual aids (PowerPoint) are highly recommended.

Depending on the class size, you may be required to make an individual or group presentation. The exact date of this assessment item will be determined by individual lecturers in consultation with the class.


Criteria & Marking:

Marks will be assigned as follows:

Content (60%): Including: Introduction and background theory, aims, methodology, results, conclusions and ANSWERS TO ALL OF THE QUESTIONS IN THE PROJECT OUTLINE
Presentation (20%): Logical flow of the ideas presented in seminar and between the individual presenters; presenters should be confident; appropriate use of visual aids and diagrams to support the presentation. Visual aids need to be clear and uncluttered, with an appropriate level of detail and overall number of slides ; 
Question & Answer (20%): Questions are answered convincingly and honestly, i.e., answering “I don’t know” is better than pretending you do


Module 4: Lab. Demonstration plus Presentation
Type: DEMO-3
Learning Objectives Assessed: 1, 2, 3, 4
Due Date:
         22 Sep 08     To be finalized with student consultations.
Weight: 15%
Task Description:

Students will be required to make a 30 minute  presentation and answer questions on the outcomes of  laboratory work. In particular students should present all of the results from the laboratory sessions and describe in detail how the algorithms are implemented in Matlab. Visual aids (PowerPoint) are highly recommended.

Depending on the class size, you may be required to make an individual or group presentation. The exact date of this assessment item will be determined by individual lecturers in consultation with the class.


Criteria & Marking:

Marks will be assigned as follows:

Content (60%): Including: Introduction and background theory, aims, methodology, results, conclusions and ANSWERS TO ALL OF THE QUESTIONS IN THE PROJECT OUTLINE
Presentation (20%): Logical flow of the ideas presented in seminar and between the individual presenters; presenters should be confident; appropriate use of visual aids and diagrams to support the presentation. Visual aids need to be clear and uncluttered, with an appropriate level of detail and overall number of slides ; 
Question & Answer (20%): Questions are answered convincingly and honestly, i.e., answering “I don’t know” is better than pretending you do


Module 5: Lab. Demonstration plus Presentation
Type: DEMO-4
Learning Objectives Assessed: 2, 3, 4
Due Date:
         20 Oct 08     To be finalized with student consultations.
Weight: 15%
Task Description:

Students will be required to make a 30 minute  presentation and answer questions on the outcomes of  laboratory work. In particular students should present all of the results from the laboratory sessions and describe in detail how the algorithms are implemented in Matlab. Visual aids (PowerPoint) are highly recommended.

Depending on the class size, you may be required to make an individual or group presentation. The exact date of this assessment item will be determined by individual lecturers in consultation with the class.


Criteria & Marking:

Marks will be assigned as follows:

Content (60%): Including: Introduction and background theory, aims, methodology, results, conclusions and ANSWERS TO ALL OF THE QUESTIONS IN THE PROJECT OUTLINE
Presentation (20%): Logical flow of the ideas presented in seminar and between the individual presenters; presenters should be confident; appropriate use of visual aids and diagrams to support the presentation. Visual aids need to be clear and uncluttered, with an appropriate level of detail and overall number of slides ; 
Question & Answer (20%): Questions are answered convincingly and honestly, i.e., answering “I don’t know” is better than pretending you do


Final Exam
Type: Exam - during Exam Period (School)
Learning Objectives Assessed: 2, 3, 4, 5, 7
Due Date:
         Examination Period
Weight: 40%
Perusal: 10 minutes
Duration: 180 minutes
Format: Problem solving
Task Description:

A  2-hour closed-book final examination will be held during the final examination period.  Assessment variation is possible for students with a disability. Use of dictionaries in exams:  Students may request the use of dictionaries, including bilingual dictionaries, supplied by Examinations Section. 


Criteria & Marking:

The final examination will be a 2 hour closed book exam with three problem -solving type questions. Problem-solving type question may have several sub-sections requiring descriptive and/or numerical answers. You will be required to answer all the questions in the paper.

You are expected to be familiar with all aspects of the course activities including the end-of-module quizes of all modules. Subject matter taught/discussed directly in the classroom (+ instrumentation laboratory and the computer labs) as well as knowledge gained through other activities such as tasks (activities) assigned in the course guides, hands-on project work (instrumentation+computer labs), and reading materials provided (or directed to) by the lectures will also be considered as examinable content.



6. Policies & Guidelines

 
This section contains the details of and links to the most relevant policies and course guidelines. For further details on University Policies please visit myAdvisor and the University Handbook of Policies and Procedures.

6.1 Assessment Related Policies and Guidelines

University Policies & Guidelines

An overview of the University’s assessment-related policies can be found on myAdvisor (http://www.uq.edu.au/myadvisor/index.html?page=2910).

Academic Integrity
It is the University's task to encourage ethical scholarship and to inform students and staff about the institutional standards of academic behaviour expected of them in learning, teaching and research. Students have a responsibility to maintain the highest standards of academic integrity in their work. Students must not cheat in examinations or other forms of assessment and must ensure they do not plagiarise.

Plagiarism
The University has adopted the following definition of plagiarism:

Plagiarism is the act of misrepresenting as one's own original work the ideas, interpretations, words or creative works of another. These include published and unpublished documents, designs, music, sounds, images, photographs, computer codes and ideas gained through working in a group. These ideas, interpretations, words or works may be found in print and/or electronic media.

Students are encouraged to read the UQ Academic Integrity and Plagiarism policy (http://www.uq.edu.au/hupp/index.html?page=25128) which makes a comprehensive statement about the University's approach to plagiarism, including the approved use of plagiarism detection software, the consequences of plagiarism and the principles associated with preventing plagiarism.

Feedback on Assessment
Feedback is essential to effective learning and students can expect to receive appropriate and timely feedback on all assessment. For a detailed explanation of the feedback you are entitled to, you should consult the policy on Student Access to Feedback on Assessment. (http://www.uq.edu.au/hupp/index.html?page=25109)

As a student you have a responsibility to incorporate feedback into your learning; make use of the assessment criteria that you are given; be aware of the rules, policies and other documents related to assessment; and provide teachers with feedback on their assessment practices.

There are certain steps you can take if you feel your result does not reflect your performance. Please refer to the myAdvisor web site. (http://www.uq.edu.au/myadvisor/index.html?page=2953)

School of Information Technology and Electrical Engineering Assessment Guidelines

Misconduct
 

Further to the statement on academic integrity and plagiarism above, students are required to read and understand the ITEE policy on Student Misconduct (http://www.itee.uq.edu.au/about_ITEE/policies/student-misconduct.html).

 

Late Arrival or Non-attendance at Examinations

 

The policy and procedure for late arrival or non-attendance at centrally controlled examinations is set out in the University's Examinations policy (HUPP 3.30.5), sections 8 and 10.2.

 

The way in which late arrival at a School-controlled examination is dealt with will be at the discretion of the course coordinator, who may be guided by the policy for centrally controlled exams.

 

In the case that a student requests a special exam for a School-controlled exam, the request will be considered and, if allowed, the timing shall be determined by the course coordinator, in consultation with the School's Chief Examiner where necessary, and in accordance with HUPP 3.30.5. Unless otherwise indicated in the Course Profile, applications must be made in writing to the Head of School no later than one week after the exam. Late applications will not be accepted.

 
Examination Feedback
 
In addition to the advice above, students wishing to view examination answer scripts and/or question papers should consult with the School office (Room 217, General Purpose South Building [78], St Lucia; Room 218, Building 1, Ipswich) regarding arrangements. The ITEE policy on exam script viewing is available at http://study.itee.uq.edu.au/current_students/exam_script_viewing.html.

Supplementary Assessment

If you fail this course you may be eligible for supplementary assessment - see the general award rules and/or your program rules for details. You should note that even though you may be eligible for supplementary assessment under these rules, in some circumstances there may be no practical assessment that can be offered to allow you to meet the minimum passing requirements. These circumstances may include failure based on:
  • group or team based assessment;
  • attendance or class participation requirements;
  • laboratory-based assessment, where laboratories can't practically be made available after classes have finished;
  • project or thesis-based assessment, where a significant period of time would be required to undertake supplementary assessment;
  • progressive assessment, where subsequent assessment items build on earlier assessment items; or
  • multiple assessment items, where it is impractical to offer multiple supplementary assessment items.
If the course coordinator determines that there is no practical supplementary assessment that can be offered to allow you to improve your grade, then you will not be offered supplementary assessment and your grade will remain unchanged.

6.2 Other Policies and Guidelines

University Policies and Guidelines

Placement Courses
Students on a placement course – also known as a work placement, internship, industry study, industry experience, clinical practice, clinical placement, practical work, practicum, fieldwork, teaching practice – should refer to the University policy, Placement Courses (http://www.uq.edu.au/hupp/index.html?page=25120) for detailed information.
 
Working with Children
Students whose studies include a professional/work placement, internship, clinical practice, teaching practice or other similar activity which involves them in regular contact with children should refer to the University policy, Working with Children Check - "blue card" (http://www.uq.edu.au/hupp/index.html?page=25004) to find out how to apply for a ‘blue card’.
 
Students with a Disability
Any student with a disability who may require alternative academic arrangements, including assessment, in the course/program is encouraged to seek advice at the commencement of the semester from a Disability Adviser at Student Support Services. Refer to the University policy, Students with a Disability (Disability Action Plan) (http://www.uq.edu.au/hupp/index.html?page=25122) and to the policy on Special Arrangements for Examinations for Students with a Disability (http://www.uq.edu.au/hupp/index.html?page=25111

Where an adjustment is made to an accredited program, it is the responsibility of the relevant Faculty to liaise with professional and registration bodies regarding the acceptability of the change/s.  

Occupational Health and Safety
Undergraduate Students (http://www.uq.edu.au/hupp/index.html?page=25055) and Postgraduate Students (http://www.uq.edu.au/hupp/index.html?page=25057) should be familiar with the University policies on occupational health and safety in the laboratory.

Other School of Information Technology and Electrical Engineering Guidelines

Ethical Clearance
If your course involves assignment or project work involving human subjects or human-related materials, you must investigate the need for ethical clearance and obtain it when required. Information on ethical clearance can be found at http://www.uq.edu.au/research/orps/index.html?page=5064&pid=5256.

Learning Summary

 

Below is a table showing the relationship between the learning objectives for this course and the broader graduate attributes developed, the learning activities used to develop each objective and the assessment task used to assess each objective.

Learning Objectives

After successfully completing this course you should be able to:

1  identify models of learning as applicable to your university and life-long education, with particular relevance to a multidisciplinary subject such as Biomedical Signal Processing.
2  understand the uses and importance of signal processing in medical instrument design and clinical practice.
3  understand the processes of sampling, quantization and filtering of clinical biomedical signals, and, elements of Stochastic Signal processing; uses in Auditory Brainstem Response analysis.
4  understand the acquisition, uses and linear & nonlinear processing of clinical signals obtained in an overnight polysomnography (PSG).
5  understand: some physiological correlates of "relaxation" and measure them within the laboratory; uses of music therapy in medicine; digital filter design and linear signal processing; experiment design.
6  Seek information independently from diverse sources (eg: the Internet, books, electronic application notes, company product data sheets, databases such as the ISI Web of Science and IEEExplore) in providing solutions to a real-world biomedical signal analysis problems.
7  apply some standard techniques of event detection and waveform shape/complexity analysis in biomedical signal processing problems.


Assessment & Learning Activities

  Learning Objectives
  1 2 3 4 5 6 7
Learning Activities
Introduction & Learning How to Learn (Discussion)
selected
       
selected
 
The Auditory Brainstem Response (ABR) (Other)  
selected
selected
   
selected
selected
Polysomnography Signals [Module 2] (Other)  
selected
 
selected
 
selected
selected
Music and Physiological Signals [Module 4] (Project based Learning)  
selected
selected
 
selected
selected
selected
Introduction to Adaptive Filtering [Module 5] (Case Study)  
selected
selected
   
selected
selected
Assessment Tasks
Module 2: Lab. Demonstration plus Presentation  
selected
selected
   
selected
 
Module 3: Lab. Demonstration plus Presentation  
selected
selected
       
Module 4: Lab. Demonstration plus Presentation
selected
selected
selected
selected
     
Module 5: Lab. Demonstration plus Presentation  
selected
selected
selected
     
Final Exam  
selected
selected
selected
selected
 
selected

Graduate Attributes

Successfully completing this course will contribute to the recognition of your attainment of the following UQ (Postgrad Coursework) graduate attributes:

  Learning Objectives
  1 2 3 4 5 6 7
Graduate Attributes
A IN-DEPTH KNOWLEDGE OF THE FIELD OF STUDY
A2. A broad understanding of the field of study, including how other disciplines relate to the field of study.
selected
selected
 
selected
selected
selected
 
A3. A comprehensive and in-depth knowledge in the field of study.    
selected
 
selected
 
selected
A5. An international perspective on the field of study.              
A7. An appreciation of the link between theory and practice.              
B EFFECTIVE COMMUNICATION
B1. The ability to collect, analyse and organise information and ideas and to convey those ideas clearly and fluently, in both written and spoken forms.
selected
 
selected
   
selected
 
B2. The ability to interact effectively with others in order to work towards a common outcome.              
B3. The ability to select and use the appropriate level, style and means of communication.              
B4. The ability to engage effectively and appropriately with information and communication technologies.              
B5. The ability to practise as part of an interdisciplinary team.
selected
   
selected
     
C INDEPENDENCE AND CREATIVITY
C2. The ability to work and learn independently and effectively.
selected
       
selected
selected
C3. The ability to generate ideas and adapt innovatively to changing environments.              
C5. The ability to formulate and investigate problems, create solutions, innovate and improve current practices.    
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C6. The abilities and skills that provide a foundation for future leadership roles.
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D CRITICAL JUDGEMENT
D2. The ability to apply critical reasoning to issues through independent thought and informed judgement.        
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selected
D4. The ability to process material and to critically analyse and integrate information from a wide range of sources.    
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selected
 
D5. The ability to evaluate opinions, make decisions and to reflect critically on the justifications for decisions using an evidence-based approach.        
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E ETHICAL AND SOCIAL UNDERSTANDING
E1. An understanding of social and civic responsibility.              
E3. An appreciation of the philosophical and social contexts of a discipline.      
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E4. A knowledge and respect of ethics and ethical standards in relation to a major area of study.              
E5. A knowledge of other cultures and times and an appreciation of cultural diversity.              
E7. The ability to work effectively and sensitively across all areas of society.              
E8. An understanding of and respect for the roles and expertise of associated disciplines.