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Related Concept Videos

Sleep Apnea01:21

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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Related Experiment Video

Updated: Jun 23, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

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Published on: December 11, 2015

Accuracy of Smartphone-Mediated Snore Detection in a Simulated Real-World Setting: Algorithm Development and

Jeffrey Brown1, Zachary Mitchell1, Yu Albert Jiang1

  • 1Bodymatter, Inc, 4343 Von Karman Ave, Suite 150J, Newport Beach, CA, 92660, United States, 1 877-870-0649.

JMIR Formative Research
|March 28, 2025
PubMed
Summary

The SleepWatch app accurately detects snoring using machine learning, achieving 95.6% overall accuracy in simulated tests. This tool may help identify individuals at risk for sleep apnea.

Keywords:
BodymatterSleepWatchmachine learningmobile devicemobile healthneural netsleep apneasleep monitoringsleep trackingsmartphonesmartphone applicationsnore detectionsnore tracking

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Last Updated: Jun 23, 2026

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Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

Area of Science:

  • Sleep science
  • Digital health technology
  • Machine learning applications

Background:

  • High-quality sleep is crucial for physical and mental health.
  • Poor sleep is linked to various health issues, including cardiometabolic diseases and increased mortality.
  • Snoring disrupts sleep and is associated with conditions like obstructive sleep apnea.

Purpose of the Study:

  • To evaluate the accuracy of the SleepWatch smartphone app's snore detection algorithm.
  • To assess the algorithm's performance in a simulated real-world environment.

Main Methods:

  • The SleepWatch algorithm was tested using 36 simulated snoring audio files (30-600 snores/hour) and 9 nonsnoring files.
  • Performance metrics included sensitivity, specificity, accuracy, positive predictive value, and negative predictive value.
  • Bland-Altman plots and Spearman correlation were used for statistical analysis.

Main Results:

  • The algorithm achieved an average accuracy of 95.2% for snoring tests and 97.1% specificity for nonsnoring sounds.
  • Overall aggregated accuracy across all tests was 95.6%.
  • Strong positive correlation (rs=0.974; P<.001) was found between detected and actual snore rates.

Conclusions:

  • The SleepWatch snore detection algorithm demonstrates high accuracy and reliability.
  • It performs comparably to other snore detection applications.
  • The app shows potential for identifying individuals at risk for sleep-disordered breathing, such as obstructive sleep apnea, based on snoring index.