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Developing Artificial Intelligence Tools for the Identification of Sleep Apnoea


Project Description

Obstructive Sleep Apnoea (OSA) occurs due to the repetitive closure of the pharynx during sleep, leading to sleep fragmentation and, often, daytime somnolence. This has major implications for driving or working with heavy machinery where staying awake and remaining vigilant is extremely important leading to increasing concern from the driving regulatory authorities (DVLA in the UK). In addition, untreated OSA (especially when moderate to severe) leads to increased hypertension, cardiovascular disease and morbidity (NICE 2008). Continuous positive airway pressure (CPAP) is an effective treatment for OSA. If the patient diagnosed with OSA after a sleep study is adherent most nights, then the risk of falling asleep during the day reduces dramatically as does their risk of a road or occupational accident. There is an unmet clinical challenge of reducing the diagnostic pathway and the need for multiple compliance visits. The overarching aim of this project is to develop new reliable and accurate prediction tools for CPAP compliance. This project aims to develop new methodologies in the field of big data and artificial intelligence (e.g. deep learning) to support biomedical applications.
The successful PhD candidate will benefit from working with a multidisciplinary team in which there exists extensive experience in the areas of computer science, high performance computing, mathematics, and medicine. All postgraduate students undertake the PGR Development Programme which aims to enhance their skills for a successful research experience and career. They are required to maintain an online record of their progress and record their personal and professional development throughout their research degree. The 1st Year Development Workshops encourage inter- and cross-disciplinary thinking and identify and develop the knowledge, skills, behaviours and personal qualities that all students require. In the 2nd year all students take part in a Poster Day to provide an opportunity to present their research to a degree educated general public, and in the 3rd year students complete a career development module. Other online training, such as ‘Managing your supervisor’ and ‘Thesis writing’ is provided centrally.

The Institute of Ageing and Chronic Disease is fully committed to promoting gender equality in all activities. In recruitment we emphasize the supportive nature of the working environment and the flexible family support that the University provides. The Institute holds a silver Athena SWAN award in recognition of on-going commitment to ensuring that the Athena SWAN principles are embedded in its activities and strategic initiatives. The Institute of Ageing and Chronic Disease is fully committed to promoting gender equality in all activities. In recruitment we emphasize the supportive nature of the working environment and the flexible family support that the University provides. The Institute holds a silver Athena SWAN award in recognition of on-going commitment to ensuring that the Athena SWAN principles are embedded in its activities and strategic initiatives.

The successful candidate should have, or expect to have an Honours Degree at 2.1 or above (or equivalent) in Mathematics, Engineering, Physics or Computer Science. It is essential to have good background knowledge in mathematics, machine learning, computer programming (e.g., Matlab, Python or C++), and signal processing plus a proactive approach to their work. Candidates whose first language is not English should have an IELTS score of 6.5 or equivalent.

Informal enquiries regarding this project should be made to Dr Yalin Zheng (). All general enquiries should be directed to Mrs Sue Jones ().

To apply please send your CV and a covering letter to Dr Zheng () with a copy to

Funding Notes

The successful applicant will be expected to provide the funding for tuition fees, bench fees of approximately £3,000 per year and all living expenses. Details of the cost of study can be found on the University website. There is NO funding attached to this project.

We have a thriving international researcher community and encourage applications from students of any nationality able to fund their own studies (Government scholarship), or who wish to apply for their own funding (e.g. China Scholarship Council).

References

1. Kervrann C, Sorzano C, Acton S, Olivo-Marin J, Unser M. IEEE Journal of Selected Topics in Signal Processing. 2016; 10: 6-30.
2. LeCun Y, Bengio Y, Hinton G. Nature. 2015;521:436-444.
3. NICE 2007. Sleep Apnoea- continuous positive airways pressure (CPAP). Available at www.nice.org.uk/guidance/ta139/resources/sleep-apnoea-continuous-positive-airways-pressure-cpap-acd-assessment-report2.
4. NICE 2008a. Continuous positive airway pressure for the treatment of obstructive sleep apnoea/hypopnoea syndrome. NICE technology appraisal guidance 139. Available at www.nice.org.uk/guidance/ta139/chapter/1-guidance.

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