Degree type
PhD
Closing date
1 April 2025
Campus
Hobart
Citizenship requirement
Domestic / International
Scholarship
$33,511pa for 3.5 years
About the research project
Accurate non-invasive monitoring of respiratory activity including detection of dangerous cardiorespiratory events is critical to the care of preterm infants who suffer from a range of respiratory conditions affecting their ability to breathe without support. Existing technologies in current clinical use are plagued by inaccuracies and false alarms caused by signal artefacts. Despite standard bedside monitors having access to a suite of high-quality physiological signals including electrocardiogram, respiratory impedance, and photoplethysmography, these signals are generally processed and displayed independently. Approaches to fuse multiple signals to obtain a more accurate estimate of the true waveform have been applied to the electrocardiogram signal for the removal of motion artefact, although this has not yet been implemented in bedside monitoring. Similar approaches could be applied to respiratory monitoring and when paired with sophisticated classification algorithms could permit the detection of episodes of central and obstructive apnoea with improved accuracy.
The performance of these algorithms could be further supplemented with the addition of non-standard respiratory activity sensors to obtain an improved respiratory signal and identify periods of central, obstructive and mixed apnoea. These technologies could be implemented with little modification to established monitoring practices but could provide substantial improvements in the quality of care by improving the accuracy of associated alarms. Sufficiently accurate detection of apnoea could also permit automatically triggered interventions to mitigate the physiological instability that may follow from an apnoeic event.
This project aims to investigate the potential for sensor fusion algorithms to provide a more accurate and reliable respiratory activity signal in preterm infants, develop algorithms for the detection of central, obstructive and mixed apnoea, and explore the potential for supplementary physiological sensors to improve the monitoring performance.
The project may include
- Use of an existing large clinical dataset to develop algorithms to combine multiple physiological signals to recover a more accurate respiratory signal.
- Development of algorithms to detect and classify clinically significant cardiorespiratory events.
- Evaluation of algorithm performance clinically in comparison with established/gold-standard algorithms.
Primary Supervisor
Funding
The successful applicant will receive a scholarship which provides:
- a living allowance stipend of $33,511 per annum (2025 rate, indexed annually) for 3.5 years
- a relocation allowance of up to $2,000
- a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)
If successful, international applicants will receive a University of Tasmania Fees Offset for up to four years.
As part of the application process you may indicate if you do not wish to be considered for scholarship funding.
Other funding opportunities and fees
For further information regarding other scholarships on offer, and the various fees of undertaking a research degree, please visit Scholarships and fees.
Eligibility
Applicants should review the Higher Degree by Research minimum entry requirements.
Additional eligibility criteria specific to this project/scholarship:
- An honours or Masters degree in Biomedical Engineering or related field
- Applicants must be able to undertake the project on-campus
Selection Criteria
The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.
Additional essential selection criteria specific to this project:
- Ability to develop electronic printed circuits for mixed signal data acquisition and signal conditioning, and to develop signal processing algorithms for noise reduction and event detection
Additional desirable selection criteria specific to this project:
- Experience in software development and algorithm implementation in Matlab and/or Python
- Experience in development of signal processing algorithms
- Authorship of high-quality journal articles
Application process
- Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
- Contact Prof Peter Dargaville to discuss your suitability and the project's requirements; and
- In your application:
- Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
- Submit a signed supervisory support form, a CV including contact details of 2 referees and your project research proposal.
- Apply prior to 1 April 2025.
Full details of the application process can be found under the 'How to apply' section at Research degrees.
Following the closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.
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