Related Experiment Video
Updated: Apr 19, 2026
07:54
Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
Published on: December 6, 2016
20.9K
SleepAp: an automated obstructive sleep apnoea screening application for smartphones
IEEE Journal of Biomedical and Health Informatics
|January 7, 2015
Summary
A new phone app, SleepAp, screens for obstructive sleep apnoea (OSA) using audio, movement, and PPG signals. This low-cost tool achieved up to 92.2% accuracy in identifying moderate to severe OSA.
Area of Science:
- Biomedical Engineering
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Obstructive sleep apnoea (OSA) is a prevalent sleep disorder linked to significant long-term health issues, including cardiovascular diseases.
- Current diagnostic methods like polysomnography are often resource-intensive, leading to high costs and lengthy wait times.
- There is a need for accessible, cost-effective screening tools for early OSA detection.
Purpose of the Study:
- To introduce a novel screening framework and a prototype mobile application for obstructive sleep apnoea.
- To develop and validate a machine learning model for OSA classification using multi-modal sensor data.
- To integrate the developed algorithms into a user-friendly smartphone application for widespread screening.
Main Methods:
- Collected a database of 856 patients who underwent at-home polygraphy.
- Extracted features from audio, actigraphy, photoplethysmography (PPG), and demographic data.
- Trained a Support Vector Machine (SVM) classifier on 735 patients and validated on 121 patients, achieving up to 92.2% accuracy.
- Integrated signal processing and machine learning algorithms into a Java-based smartphone application (SleepAp).
Main Results:
- The SVM classifier demonstrated high accuracy (up to 92.2%) in distinguishing moderate to severe OSA from healthy or snoring individuals.
- The SleepAp application successfully integrates data collection (body position, audio, actigraphy, PPG) and the STOP-BANG questionnaire.
- The application utilizes trained machine learning models to classify users for OSA based on collected physiological signals.
Conclusions:
- The developed SleepAp application offers a promising new method for OSA screening.
- This mobile health solution is easy-to-use, low-cost, and widely accessible, potentially improving early detection rates.
- The study highlights the potential of smartphone-based technology and machine learning in managing sleep disorders.
Related Concept Videos
01:21Sleep Apnea
846
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...
The condition is more prevalent among...
846
01:22Cardiopulmonary Resuscitation II: ACLS Airway Management
1.1K
Airway management is a key skill in emergency and critical care settings, as maintaining a clear airway is essential for adequate oxygenation and ventilation.Head Tilt-Chin Lift TechniqueThe head tilt-chin lift maneuver is an essential technique primarily used in patients without suspected cervical spine injuries. To perform this maneuver, one hand is placed on the patient’s forehead, and gentle pressure is applied backward to tilt the head. The fingertips of the other hand are positioned...
1.1K