New University research could make detecting sleep apnoea symptoms easier
New wearable technology is providing fresh insights into how to monitor sleep patterns and tackle sleep apnoea, according to research by the University of Chester.
Sleep apnoea is a condition where a person stops breathing when they sleep, with the most common cause being obstructive sleep apnoea. Symptoms include someone’s breathing stopping and starting, making gasping, snorting or choking noises, loud snoring, and feeling very tired during the day. Left untreated, it can lead to more serious medical problems in the long term, and those undergoing diagnostic tests can be referred to specialist sleep clinics.
However, such methods can require patients to undergo testing in sleep laboratories while wearing multiple sensors, which can be difficult for the patient financially, logistically, and trying to get an effective natural sleep pattern. Such sleep tests also usually rely on data analysis after the patient has slept, rather than detecting sleep apnoea symptoms as they happen.
A new study by the University of Chester is proposing a more effective treatment. The study, titled A Wearable AI-Driven System for Real-Time Detection of Sleep Apnoea, would see patients wearing a device which includes multiple sensors that continuously monitor respiratory activity, heart rate, blood oxygen saturation levels and even body posture.
The PhD award-winning report was written by Electronic and Electrical Engineering Research Degree graduate Dr Yurui Zheng and advised by Professor Bin Yang and Associate Professor Dr Theo Papadopoulos, and was made in collaboration with PFL Healthcare. It found the AI deep learning trained model achieved an apnoea detection precision of over 95%. The real-time results also enable timely visual feedback and potential activations of therapeutic stimulators to address the sleep apnoea.
The system architecture includes AI model deployment, mobile application development, and cloud-based storage infrastructure to support continuous monitoring, model updates, and remote analysis.
Overall, this research contributes a scalable, cost-effective, and user-friendly solution for non-invasive, real-time sleep apnoea monitoring and intervention, bridging the gap between clinical diagnostics and home-based sleep health management.
Yurui explained what the research could mean to those with sleep problems.
He said: “Sleep apnoea remains underdiagnosed due to the high cost and slow access to traditional testing. Some advanced home diagnostics cost around £348. Clinical in-lab PSG starts at £880 and takes roughly 10 days. NHS pathways are often slower, taking months and multiple appointments.
“The vision for wearable AI in sleep care is to create a seamless, non-invasive solution that continuously monitors sleep patterns, detects anomalies, and even intervenes to improve sleep quality. I'm excited about the potential to translate cutting-edge AI into tangible health benefits – empowering people to manage sleep disorders proactively, anytime, anywhere."
Dr Theo Papadopoulos said: “This research highlights how engineering and AI can directly address real-world healthcare challenges.
“Congratulations to Dr Zheng and Prof Yang on developing a scalable, patient-centred, and cost-effective solution that bridges the gap between the clinical laboratory and everyday wellbeing.”
Main image: Dr Yurui Zhenh.