Clinical Validation
SleepAI's technology has been rigorously validated through peer-reviewed research and clinical studies in partnership with leading medical institutions.
Key Validation Metrics
Our technology has been validated across multiple clinical parameters
89%
OSA Detection Accuracy
The highest accuracy in the field for sleep apnea detection compared to gold-standard PSG.
0.75
Sleep Staging Kappa
Excellent agreement with EEG-based scoring for sleep stage classification.
10,000+
Retrospective Tests
Our algorithms have been trained and validated on over 10,000 nights of sleep data.
3
Peer-Reviewed Papers
Our research is published in top-tier scientific journals, ensuring transparency and validation.
Peer-Reviewed Publications
Our research has been published in leading medical and engineering journals
Clinical Validation of Artificial Intelligence Algorithms for the Diagnosis of Adult Obstructive Sleep Apnea and Sleep Staging From Oximetry and Photoplethysmography—SleepAI
Shirel Attia, et al.
Journal of Sleep Research • 2024
Clinical validation study of SleepAI system showing 89% accuracy for OSA severity classification and Cohen's kappa of 0.75 for sleep staging. Study conducted with 53 subjects using both home and lab-based testing.
Read Full PaperSleepPPG-Net: A Deep Learning Algorithm for Robust Sleep Staging From Continuous Photoplethysmography
Kevin Kotzen, et al.
IEEE Journal of Biomedical and Health Informatics • 2023
Deep learning model for 4-class sleep staging from raw PPG time series, achieving Cohen's Kappa score of 0.75. Trained on 2,374 patients and 23,055 hours of continuous data.
Read Full PaperDeep learning for obstructive sleep apnea diagnosis based on single channel oximetry
Jeremy Levy, et al.
Nature Communications • 2023
OxiNet deep learning model for OSA diagnosis from oximetry signals. Developed using 12,923 polysomnography recordings from six independent databases with superior performance over benchmarks.
Read Full PaperReady to Experience SleepAI?
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