Wearable Patch Uses Machine Learning to Detect Sleep Apnea

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Wearable Patch Uses Machine Learning to Detect Sleep Apnea
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Wearable Patch Uses Machine Learning to Detect Sleep Apnea
A new device could make it easier to monitor sleep apnea at home
Photo: Onera Health Inc This prototype patch by Onera Health relies on machine learning and bioimpedence measurements to detect sleep apnea.
Getting screened for sleep apnea often means spending a night in a special clinic hooked up to sensors that measure your brain activity, eye movement, and blood oxygen levels.
But for long-term, more convenient monitoring of sleep apnea, a team of researchers has developed a wearable device that tracks a user’s breathing.
The device, described in a study published 20 January in the IEEE Journal of Biomedical and Health Informatics, uses a unique combination of bioimpedance (a measurement of electrical signals passing through the body) and machine learning algorithms.
Sleep apnea is a condition whereby a person’s breathing can be disrupted as they sleep, either from physical obstruction of the throat by surrounding muscles, or when the neural signals controlling their breathing are disturbed.
To monitor the condition more easily at home, various devices have been created that measure breathing using resistive bands around the chest or abdomen, film-based sensors, microelectronic systems, and even wearable piezo-electric bands.
In the latest advance, a group of researchers at imec and Ghent University, who had previously developed a device that measures bioimpedance, sought to explore whether the technique could also be used to monitor the breathing patterns of people with sleep apnea.
The team then applied deep learning algorithms to the bioimpedance measurements to detect sleep apnea events.
They compared results of their technique to data from 25 volunteers who were monitored at a sleep clinic, and found that their approach has an accuracy of 73 percent at detecting sleep apnea events

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