AI-PoweredSleep Disorders Detection
Clinically validated AI technology that analyzes data from wearable sensors to detect respiratory events with unparalleled accuracy and affordability.
Comprehensive Sleep Analysis Features
Our AI-powered platform offers a complete suite of tools for accurate sleep disorder diagnosis and monitoring
Capabilities
Sleep Stage Analysis
Precise classification of sleep stages (REM, Light, Deep) with a Kappa score of 0.75, comparable to clinical standards.
CVD Risk Indicators
Provides informational insights into cardiovascular disease risk factors based on sleep physiological signals, supporting proactive wellness and preventive care discussions.
Hypoxic Burden
Measures the total impact of oxygen desaturation, providing a key metric for cardiovascular risk.
Respiratory Events Detection
Detects and analyzes respiratory events during sleep, providing insights into breathing patterns and irregularities.
Technology & Integration
Rapid Processing
Results are available in minutes after data upload, a significant improvement over the weeks-long wait for PSG results.
Wearable Integration
Seamless compatibility with popular consumer wearables and medical-grade sensors.
How SleepAI Works
Our technology transforms simple wearable data into clinical-grade sleep apnea detection
Data Collection
User wears a compatible device (pulse oximeter, smart ring, or other sensor) during sleep to collect physiological data.
AI Analysis
Our proprietary algorithms analyze oxygen levels, heart rate variability, movement, and other biosignals to detect sleep disturbances.
Clinical Results
Physicians receive detailed reports with respiratory events detection, sleep staging, and other clinically relevant metrics to guide diagnosis and treatment.
Backed by Rigorous Scientific Research
Our AI algorithms have been extensively validated in clinical settings to ensure reliability and accuracy
Publications
Clinical Validation of Artificial Intelligence Algorithms for the Diagnosis of Adult Obstructive Sleep Apnea and Sleep Staging From Oximetry and Photoplethysmography—SleepAI
Journal of Sleep Research, 2024
89% accuracy for OSA severity classification with Cohen's kappa of 0.75 for sleep staging
SleepPPG-Net: A Deep Learning Algorithm for Robust Sleep Staging From Continuous Photoplethysmography
IEEE Journal of Biomedical and Health Informatics, 2023
Achieved Cohen's Kappa score of 0.75 for 4-class sleep staging from raw PPG time series
Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry
Nature Communications, 2023
OxiNet missed only 0.2% of moderate-to-severe OSA patients vs 21% for best benchmark
Our Commitment
We are committed to the highest standards of scientific rigor. Our technology is built on a foundation of peer-reviewed research and continuous validation.
Our technology has been validated in partnership with leading medical institutions
Ready to Transform Sleep Apnea Detection?
Join leading healthcare providers and technology companies using SleepAI to deliver better sleep health.