ECG and SpO2 Signal-Based Real-Time Sleep Apnea Detection Using Feed-Forward Artificial Neural Network

Sleep apnea (SA) is a common sleep disorder characterized by respiratory disturbance during sleep. Polysomnography (PSG) is the gold standard for apnea diagnosis, but it is time-consuming, expensive, and requires manual scoring. As an alternative to PSG, we investigated a real-time SA detection syst...

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Bibliographic Details
Published in:AMIA ... Annual Symposium proceedings 2022-05, Vol.2022, p.379-385
Main Authors: Paul, Tanmoy, Hassan, Omiya, Alaboud, Khuder, Islam, Humayera, Rana, Md Kamruz Zaman, Islam, Syed K., Mosa, Abu S. M.
Format: Article
Language:English
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Summary:Sleep apnea (SA) is a common sleep disorder characterized by respiratory disturbance during sleep. Polysomnography (PSG) is the gold standard for apnea diagnosis, but it is time-consuming, expensive, and requires manual scoring. As an alternative to PSG, we investigated a real-time SA detection system using oxygen saturation level (SpO 2 ) and electrocardiogram (ECG) signals individually as well as a combination of both. A series of R-R intervals were derived from the raw ECG data and a feed-forward deep artificial neural network is employed for the detection of SA. Three different models were built using 1-minute-long sequences of SpO 2 and R-R interval signals. The 10-fold cross-validation result showed that the SpO 2 -based model performed better than the ECG-based model with an accuracy of 90.78 ± 10.12% and 80.04 ± 7.7%, respectively. Once combined, these two signals complemented each other and resulted in a better model with an accuracy of 91.83 ± 1.51%.
ISSN:1559-4076