Wearable Patch Uses Machine Learning to Detect Sleep Apnea

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

2 min read
A white patch is attached to the center of a person's bare chest.
This prototype patch by Onera Health relies on machine learning and bioimpedence measurements to detect sleep apnea.
Photo: Onera Health Inc

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. The condition can impair quality of life and, in some cases, lead to cardiovascular disease, memory problems, and other health complications.

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. 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.

An image shows a chipset designed for a device that detects sleep apnea. This chipset developed by researchers at imec is designed for a wearable device called Robin that detects sleep apnea.Photo: IMEC

With those results, the collaborators have created a new startup to commercialize the product. Van Steenkiste says that while other devices rely on elastic bands or MEMS sensors to indirectly monitor a person’s breathing from the outside, their product measures changes within the body, and is therefore likely to be more reliable.

However, many professional guidelines for assessing sleep apnea—for example, those set forth by the American Academy of Sleep Medicine, require several additional physiological signals (e.g., blood oxygen levels) to be monitored as well. More work will need to be done to combine this technology with ways to monitor these other physiological signals, Van Steenkiste notes.

“The results [of our study] also suggest that this technology could potentially be used to detect other types of respiratory events as well,” says Van Steenkiste. “The team is currently researching and exploring other respiratory conditions where this technology could be useful.”

This post was updated on 9 February 2020.

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Restoring Hearing With Beams of Light

Gene therapy and optoelectronics could radically upgrade hearing for millions of people

13 min read
A computer graphic shows a gray structure that’s curled like a snail’s shell. A big purple line runs through it. Many clusters of smaller red lines are scattered throughout the curled structure.

Human hearing depends on the cochlea, a snail-shaped structure in the inner ear. A new kind of cochlear implant for people with disabling hearing loss would use beams of light to stimulate the cochlear nerve.

Lakshay Khurana and Daniel Keppeler

There’s a popular misconception that cochlear implants restore natural hearing. In fact, these marvels of engineering give people a new kind of “electric hearing” that they must learn how to use.

Natural hearing results from vibrations hitting tiny structures called hair cells within the cochlea in the inner ear. A cochlear implant bypasses the damaged or dysfunctional parts of the ear and uses electrodes to directly stimulate the cochlear nerve, which sends signals to the brain. When my hearing-impaired patients have their cochlear implants turned on for the first time, they often report that voices sound flat and robotic and that background noises blur together and drown out voices. Although users can have many sessions with technicians to “tune” and adjust their implants’ settings to make sounds more pleasant and helpful, there’s a limit to what can be achieved with today’s technology.

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