{"id":9587,"date":"2024-10-07T10:45:05","date_gmt":"2024-10-07T08:45:05","guid":{"rendered":"https:\/\/www.uni.lu\/lcsb-en\/?post_type=news&p=9587"},"modified":"2025-02-17T15:27:21","modified_gmt":"2025-02-17T14:27:21","slug":"diagnosing-sleep-apnoea-at-home","status":"publish","type":"news","link":"https:\/\/www.uni.lu\/lcsb-en\/news\/diagnosing-sleep-apnoea-at-home\/","title":{"rendered":"Diagnosing sleep apnoea at home"},"content":{"rendered":"\n
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Sleep apnoea, a condition marked by pauses in breathing during sleep, affects millions of people worldwide, leading to severe health risks, including heart disease, stroke, and diabetes. Traditionally, diagnosing this condition requires an overnight stay at a clinic for polysomnography (PSG) tests, which are costly, complex, and uncomfortable as they involve attaching multiple sensors to the body, making it difficult to sleep naturally. In addition, limited availability of sleep clinics can lead to long waiting times for appointments which can delay the diagnosis. But what if there was a way to monitor and diagnose sleep apnoea from the comfort of your own bed? The team of Prof. Jorge Gon\u00e7alves<\/a> aims to revolutionise how sleep apnoea is diagnosed by enabling patients to monitor their sleep at home using simple, comfortable sensors.<\/p>\n\n\n\n

By analysing more than 14,000 sleep study recordings from the National Sleep Research Resource<\/a>, the researchers from the Luxembourg Centre for Systems Biomedicine<\/a> (LCSB) at the University of Luxembourg developed DRIVEN, a breakthrough method that uses wearable sensors and artificial intelligence (AI) to estimate the severity of apnoea without the need for elaborate equipment and overnight stay at the clinic. The study was recently published in npj Digital Medicine<\/em><\/a>, one of the leading scientific journals in this field, and highlights DRIVEN’s potential to make apnoea diagnosis more accessible and reduce the burden on healthcare systems.<\/p>\n\n\n\n

\nAI in action for sleep analysis<\/strong><\/h3>\n\n\n\n

The DRIVEN method uses artificial intelligence to analyse just a few signals \u2014 like abdominal movement, thoracic movement, and pulse oximetry \u2014 captured by easy to wear sensors. These sensors can be used at home and provide enough data to estimate the Apnoea-Hypopnea Index (AHI), a key metric used by doctors to determine the severity of sleep apnoea consisting of the number of apnoea events per hour. The AHI index is divided into four categories: healthy, mild, moderate and severe. The researchers compared the AHI score generated by DRIVEN to the gold standard in which a doctor analyses the polysomnography data from the sleep lab. \u201cBased on data from only a single night of sleep, using just two sensors, we were able to place 99.3% of patients either in the correct AHI category (72.4%) or one class away from the true one (26,9%),\u201d explains Gabriela Retamales<\/a>, PhD student in the AI Modelling and Prediction<\/a> group at the University\u2019s LCSB. \u201cIf people were to wear those sensors for several nights in a row, we expect the accuracy to further improve.\u201d<\/p>\n\n\n\n

\nWhy are wearable sensors a game-changer?<\/strong><\/h3>\n\n\n
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Traditional PSG tests involve attaching around 25 sensors with cables to the body, making it difficult for patients to sleep naturally.

DRIVEN, on the other hand, achieves high accuracy with minimal equipment.

The researchers found that the best combination involved using an abdominal movement sensor and pulse oximetry, allowing DRIVEN to measure breathing patterns and oxygen levels with minimal intrusion.<\/p>\n\n\n\n