Biography
Dr Shaun Davidson is a biomedical engineer in the Biomedical Signal Processing and Machine Learning (BSP-ML) group and Junior Research Fellow at Kellogg College. He has research experience across institutions in New Zealand, Belgium, and the United Kingdom and a track record of working on translational, collaborative projects with clinicians to benefit patients throughout his research career.
Prior to joining the IBME, Shaun completed his bachelor’s and PhD in Mechanical Engineering at the University of Canterbury, New Zealand, along with two research internships at the University of Liège, Belgium. While at Canterbury, he worked on real-time clinical modelling and devices for mechanical ventilation, cardiovascular monitoring, and management of insulin in the Intensive Care Unit (ICU).
Most Recent Publications
DECOVID: A UK Two-Center Harmonized Database of Acute Care Electronic Health Records for COVID-19 Research
DECOVID: A UK Two-Center Harmonized Database of Acute Care Electronic Health Records for COVID-19 Research
Longitudinal Cardiorespiratory Wearable Sleep Staging in the Home
Longitudinal Cardiorespiratory Wearable Sleep Staging in the Home
Is it time to revisit the scoring of Slow Wave (N3) Sleep?
Is it time to revisit the scoring of Slow Wave (N3) Sleep?
Sleep staging using wearables and deep neural networks
Sleep staging using wearables and deep neural networks
Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure
Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure
Research Interests
Shaun’s research interests involve developing novel devices and machine learning models for the longitudinal monitoring of individual physiology. His current research at Oxford’s Institute of Biomedical Engineering (IBME) involves the development and validation of novel machine learning methods for wearable sleep staging and the detection of illness. He is particularly passionate about the potential of using wearable sleep monitoring to better track mental health and neurodegenerative disease and titrate care.
Research Groups
Biomedical Signal Processing and Machine Learning (BSP-ML)
Related Academics
Most Recent Publications
DECOVID: A UK Two-Center Harmonized Database of Acute Care Electronic Health Records for COVID-19 Research
DECOVID: A UK Two-Center Harmonized Database of Acute Care Electronic Health Records for COVID-19 Research
Longitudinal Cardiorespiratory Wearable Sleep Staging in the Home
Longitudinal Cardiorespiratory Wearable Sleep Staging in the Home
Is it time to revisit the scoring of Slow Wave (N3) Sleep?
Is it time to revisit the scoring of Slow Wave (N3) Sleep?
Sleep staging using wearables and deep neural networks
Sleep staging using wearables and deep neural networks
Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure
Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure
Most Recent Publications
DECOVID: A UK Two-Center Harmonized Database of Acute Care Electronic Health Records for COVID-19 Research
DECOVID: A UK Two-Center Harmonized Database of Acute Care Electronic Health Records for COVID-19 Research
Longitudinal Cardiorespiratory Wearable Sleep Staging in the Home
Longitudinal Cardiorespiratory Wearable Sleep Staging in the Home
Is it time to revisit the scoring of Slow Wave (N3) Sleep?
Is it time to revisit the scoring of Slow Wave (N3) Sleep?
Sleep staging using wearables and deep neural networks
Sleep staging using wearables and deep neural networks
Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure
Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure
Awards and Prizes
- Better Sleep Pump Priming Grant for Comparing Wearable Cardiorespiratory Sleep Staging Against Polysomnogram in the Sleep Lab and at Home, 2025
- John Fell Award for Are Changes in Sleep Patterns Associated with Psilocybin Efficacy in Treatment-Resistant Depression?, 2025
- Best Online Poster Presentation, Computers in Cardiology; 13-16 September 2020
- Science for Technological Innovation (SFTI) Post-Doctoral Fellowship, 2018
Most Recent Publications
DECOVID: A UK Two-Center Harmonized Database of Acute Care Electronic Health Records for COVID-19 Research
DECOVID: A UK Two-Center Harmonized Database of Acute Care Electronic Health Records for COVID-19 Research
Longitudinal Cardiorespiratory Wearable Sleep Staging in the Home
Longitudinal Cardiorespiratory Wearable Sleep Staging in the Home
Is it time to revisit the scoring of Slow Wave (N3) Sleep?
Is it time to revisit the scoring of Slow Wave (N3) Sleep?
Sleep staging using wearables and deep neural networks
Sleep staging using wearables and deep neural networks
Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure
Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure
Teaching
- Module Lead for “Remote Monitoring and Digital Diagnostics”, MSc in Applied Digital Health
Talks
- Invited Guest Interview, American Sleep Research Society Podcast (Episode 30): https://sleepresearchsocietypodcast.podbean.com/e/srs-podcast-30-age-and-sex-bias-from-slow-wave-sleep-scoring-criteria-w-drs-shaun-davidson-monika-haack/
Most Recent Publications
DECOVID: A UK Two-Center Harmonized Database of Acute Care Electronic Health Records for COVID-19 Research
DECOVID: A UK Two-Center Harmonized Database of Acute Care Electronic Health Records for COVID-19 Research
Longitudinal Cardiorespiratory Wearable Sleep Staging in the Home
Longitudinal Cardiorespiratory Wearable Sleep Staging in the Home
Is it time to revisit the scoring of Slow Wave (N3) Sleep?
Is it time to revisit the scoring of Slow Wave (N3) Sleep?
Sleep staging using wearables and deep neural networks
Sleep staging using wearables and deep neural networks
Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure
Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure
