This month we profile PhD student Stephen McCloskey. Stephen’s research is a data-driven multidisciplinary project analysing sleep disorders using machine learning techniques to identify quantitative characteristics.
Please tell us a little about yourself
I am a current PhD student at University of Sydney that was born and raised in Sydney. I graduated in 2016 from University of Sydney with a combined degree in Bachelor of Information Technology (Honours) and Bachelor of Science.
Through my studies, I worked with the Alertness CRC and the Brain Dynamics Group investigating sleep dynamics, who were developing a sleep-wake model to assist with issues including sleepiness and adaption during jet lag and shift work. In that project, I investigated and incorporated the altering effects of light on sleep in the sleep-wake model.
What is your research on?
Currently in sleep medicine, clinicians subjectively diagnose sleep disorders such as insomnia, however the quantitative characteristics of many sleep disorders have not been determined. My research is a data-driven multidisciplinary project analysing sleep disorders using machine learning techniques to identify quantitative characteristics.
My project focuses on identifying characteristics of sleep disorders including insomnia disorder and OSA using PSG. This includes identifying potential phenotypes of insomnia disorder using EEG to assist diagnosis of insomnia, identifying hypopnea and obstructive apnea events in nasal airflow and investigating the quantitative characteristics of sleep stages using sleep trajectories from a neural-field brain model.
What are the real world consequences of your research?
The vision of this project is to identify the main characteristics of different sleep disorders to allow more accessible and cheaper personal devices at home to monitor abnormal states of sleep. This would provide patients with an objective diagnosis that could be used to assist with a more precise and personalised treatment of these sleep disorders. This could also be used to provide larger scale feedback with real-world data that can be used to further improve the diagnosis and treatment of these disorders.
What does digital health mean to you?
For me, digital health refers to the union of technology and healthcare that allows people to monitor and manage their own healthcare with a greater accuracy and with more objectivity using data-driven approaches. For many medical conditions, including sleep disorders with tiredness, doctors currently work with subjective complaints, as it is difficult or expensive for people to access the current studies used analyse those conditions, i.e. it can take weeks of preparation for an overnight sleep lab.
Digital health assists with this by investigating how to make healthcare more accessible so that can be used at home to track and identify health issues with objective measurements, which allows people to make informed decisions and make medicine more personalised and precise to their needs.
Digital health also allows for more real-world data to be generated, which allows for larger scale data-driven approaches to allow researchers to further improve their understanding and treatment of different health problems, particularly uncommon or rare conditions. This can be used to improve research into these conditions more effectively than isolated studies set in precise environments that may not be realistic for many people.
A very big thank to you to Stephen for being our June profile.