ECG and SpO < sub > 2 < /sub > Signal-Based Real-Time Sleep Apnea Detection Using Feed-Forward Artificial Neural Network

AMIA Annu Symp Proc. 2022 May 23;2022:379-385. eCollection 2022.ABSTRACTSleep 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 (SpO2) 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 SpO2 and R-R interval signals. The 10-fold cross-validation result showed that the SpO2-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%.PMID:35854719 | PMC:PMC9285163

Source: AMIA Annual Symposium Proceedings - July 20, 2022 Category: Bioinformatics Authors: Tanmoy Paul Omiya Hassan Khuder Alaboud Humayera Islam Md Kamruz Zaman Rana Syed K Islam Abu S M Mosa Source Type: research