Putting Sleep Apnea to Bed

Snore no more with an at-home wireless sleep monitoring patch that uses machine learning to accurately predict if you have sleep apnea.

Nick Bild
4 months agoHealth & Medical Devices
Detecting sleep apnea with an unobtrusive face patch (📷: S. Kwon et al.)

Sleep apnea is a common yet often undiagnosed sleep disorder that affects millions of people worldwide. It is characterized by recurrent pauses in breathing or shallow breaths during sleep. These interruptions in breathing can last for a few seconds to a minute and can occur numerous times throughout the night, disrupting the sleep cycle and leading to various health consequences.

The effects of sleep apnea on individuals can be significant and far-reaching. People with sleep apnea often experience excessive daytime sleepiness, fatigue, and poor concentration due to the interrupted sleep patterns. They may also suffer from loud and chronic snoring, morning headaches, and irritability. Furthermore, sleep apnea has been linked to an increased risk of cardiovascular problems, such as high blood pressure, heart disease, and stroke. It can also contribute to metabolic disorders like obesity and diabetes.

Diagnosing sleep apnea can be challenging and typically requires a specialized sleep study known as polysomnography. This test involves spending a night in a sleep clinic, where various sensors are used to monitor brain activity, heart rate, oxygen levels, and other physiological parameters while the person sleeps. Unfortunately, this diagnostic process can be cumbersome, expensive, and inconvenient for many individuals. The need for an overnight stay at a clinic, coupled with the cost of the sleep study, may deter people from seeking a formal diagnosis. As a result, many cases of sleep apnea go undiagnosed, and individuals continue to suffer from its consequences unknowingly.

An at-home wireless sleep monitoring patch for the clinical assessment of sleep quality and sleep apnea recently developed by researchers at Georgia Tech may soon help uncover many undiagnosed sleep disorders. Aside from being usable in the comfort of one’s own home, it also allows for normal sleep, with no concern for probes or wires that can be disconnected, or bulky, uncomfortable equipment.

The face-mounted patches are made of soft silicone, with one fitting over the forehead, and another smaller patch on the chin. Embedded within the patches are sensors designed to detect brain, eye, and muscle signals relevant to sleep quality. The measurements captured by the sensors were comparable in performance to polysomnography, which is the gold standard test for sleep apnea.

The device also contains the hardware needed to wirelessly transmit the data it captures to external processing devices, like smartphones, via a Bluetooth connection. Those external systems can then run embedded machine learning algorithms to assess the sensor data. An accuracy rate of 88.5% was observed when using this method to diagnose sleep apnea patients. The algorithm also provides a score that predicts if the individual is getting enough quality sleep.

It was also noted that even if a person does not presently have sleep apnea, the information provided by the device can predict the likelihood that it will develop in the future. This knowledge can be used to initiate lifestyle changes, like making dietary changes or altering one’s sleep patterns, to prevent development of the condition at a later date.

This at-home device certainly offers many benefits over traditional sleep studies, and it has also been shown to be far more accurate than existing at-home solutions, like headbands and smartphone apps. The widespread testing that this technology could enable has the potential to significantly improve human well-being and reduce strains on the healthcare system.

Nick Bild
R&D, creativity, and building the next big thing you never knew you wanted are my specialties.
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