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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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Related Experiment Video

Updated: Jun 15, 2025

Multi-Modal Home Sleep Monitoring in Older Adults

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

Developing probabilistic ensemble machine learning models for home-based sleep apnea screening using overnight SpO2

¹Ubiquitous and Personal Computing Lab, Kyoto University of Advanced Science (KUAS), 18 Yamanouchi Gotanda-cho, Ukyo-ku, Kyoto, Japan. liang.zilu@kuas.ac.jp.

Sleep & Breathing = Schlaf & Atmung

|August 27, 2024

Summary

This summary is machine-generated.

This study developed effective sleep apnea screening models using overnight SpO2 data, showing that higher data granularity improves performance. These models outperform existing methods, highlighting the importance of detailed SpO2 measurements for accurate sleep apnea detection.

Keywords:

Data granularity Decision boundary Ensemble learning Machine learning Oximeter Probabilistic learning Sleep apnea SpO2

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A Model to Simulate Clinically Relevant Hypoxia in Humans

A Model to Simulate Clinically Relevant Hypoxia in Humans

Published on: December 22, 2016

Area of Science:

Sleep Medicine
Biomedical Engineering
Machine Learning in Healthcare

Background:

Sleep apnea is a common disorder affecting millions globally.
Accurate screening is crucial for timely diagnosis and treatment.
Overnight SpO2 data offers a potential non-invasive method for sleep apnea screening.

Purpose of the Study:

To develop and validate machine learning models for sleep apnea screening using overnight SpO2 data.
To assess the impact of SpO2 data granularity on the performance of these screening models.

Main Methods:

Utilized 7,718 SpO2 recordings from the SHHS and MESA datasets.
Employed probabilistic ensemble machine learning to predict sleep apnea based on Apnea-Hypopnea Index (AHI) cutoffs (≥5, ≥15, ≥30).
Investigated SpO2 data aggregation at 30, 60, and 300-second intervals.

Main Results:

Achieved high Area Under the Curve (AUC) values (0.91-0.96) at 1-second granularity for internal testing.
Demonstrated good to excellent sensitivity and specificity across all AHI cutoffs.
External testing showed slightly reduced but still strong performance (AUC > 0.80).
A data granularity of 300 seconds significantly reduced performance metrics.

Conclusions:

Developed superior sleep apnea screening models compared to existing methods.
Model performance is sensitive to SpO2 data granularity, with finer resolution yielding better results.
Further research is needed to optimize screening with lower data granularity.