Wearable tech creates user-friendly sleep apnoea test
New wearable technology developed by the University of Chester is providing fresh insights into how to monitor sleep patterns and tackle sleep apnoea, with the aid of AI deep learning trained model devices able to analyse results in real time.
Current treatments frequently require patients to undergo testing in sleep laboratories while wearing multiple sensors, which can be challenging financially and logistically.
But an award-winning PhD report by Electronic and Electrical Engineering Research Degree graduate Dr Yurui Zheng (pictured) outlined how a model equipped with multiple sensors could continuously monitor respiratory activity, heart rate, blood oxygen saturation levels and body posture in real time.
Zheng was advised by Professor Bin Yang and Associate Professor Dr Theo Papadopoulos. His model, made in collaboration with PFL Healthcare, achieved an apnoea detection precision of more than 95% in tests, claimed the study.
Real-time results also enabled visual feedback and potential activations of therapeutic stimulators to address the sleep apnoea, it added.
“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,” stated Zheng.
“I'm excited about the potential to translate cutting-edge AI into tangible health benefits – empowering people to manage sleep disorders proactively, anytime, anywhere."
Sleep apnoea remained underdiagnosed not only on account of slow access to traditional testing but also due to the high cost of home diagnostics, he explained.
“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,” said Zheng.
Sleep apnoea is a condition where a person stops breathing when they sleep, with the most common cause being obstructive sleep apnoea. Left untreated, it can lead to more serious medical problems in the long term.
The Chester team said the research contributed to a scalable, cost-effective, and user-friendly solution for non-invasive, real-time sleep apnoea monitoring and intervention. This, they said, could bridge the gap between clinical diagnostics and home-based sleep health management.
Commented 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.”
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