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Sleep Apnea

Sleep Apnea

Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...

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Updated: May 6, 2026

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea

Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea

Published on: December 6, 2016

Artificial intelligence augmented home sleep apnea testing device study (AISAP study).

Sunil Sharma¹, Kassandra Olgers¹, Scott Knollinger²

¹Division of Pulmonary, Critical Care and Sleep Medicine, West Virginia University, Morgantown, WV, United States of America.

|May 17, 2024

Summary

This summary is machine-generated.

A new home sleep apnea testing device (WVU-device) shows high accuracy in detecting sleep apnea events compared to an approved device. This artificial intelligence-augmented technology performs well across diverse patient populations, including those with darker skin tones.

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Last Updated: May 6, 2026

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Multi-Modal Home Sleep Monitoring in Older Adults

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

Area of Science:

Sleep Medicine
Medical Device Technology
Artificial Intelligence in Healthcare

Background:

Home sleep apnea testing (HSAT) is crucial for diagnosing sleep-related breathing disorders.
Existing HSAT devices vary in accuracy and may face challenges with diverse patient demographics.
The WVU-device utilizes novel, patent-pending technology for enhanced sleep apnea assessment.

Purpose of the Study:

To prospectively validate the performance of the artificially augmented WVU-device for home sleep apnea testing.
To compare the WVU-device's accuracy against a commercially available, CMS-approved HSAT device.
To assess the WVU-device's efficacy across different patient characteristics, including skin tone.

Main Methods:

Prospective validation study involving 78 consecutive patients.
Simultaneous use of the WVU-device and a standard HSAT device on separate hands.
Comparison of oxygen desaturation index (ODI) from WVU-device with respiratory event index (REI) from HSAT device.

Main Results:

High correlation between WVU-device ODI and HSAT device REI, with no significant bias.
Accuracy rates of 87% (for events >=5), 89% (for events >=15), and 95% (for events >=30).
Excellent sensitivity (0.91-0.94) and specificity (0.78-0.95) across different event thresholds.

Conclusions:

The WVU-device demonstrates strong accuracy in predicting respiratory events compared to an approved HSAT device.
The device's performance is reliable, even in patients with darker skin tones (Fitzpatrick score >=3).
The WVU-device represents a promising advancement in accessible and accurate home sleep apnea diagnostics.