Wearable Patch Uses Machine Learning to Detect Sleep Apnea

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A new device could make it easier to monitor sleep apnea at home.

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.

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. Their device, called Robin, applies a small current to the body at a known frequency, and measures the resulting voltage at a different location after it passes through the body. As it turns out, Robin can be used to fairly accurately monitor a wearer’s breathing.

“When a patient breathes, air enters the lungs and the chest expands, resulting in impedance changes in the chest,” explains Tom Van Steenkiste, a researcher involved in the study. “By measuring bioimpedance on the chest… respiration can be estimated.”

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.

Get the full story at spectrum.ieee.org.

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