TY - JOUR
T1 - ECG and SpO2 Signal-Based Real-Time Sleep Apnea Detection Using Feed-Forward Artificial Neural Network
AU - Paul, Tanmoy
AU - Hassan, Omiya
AU - Alaboud, Khuder
AU - Islam, Humayera
AU - Rana, Md Kamruz Zaman
AU - Islam, Syed K.
AU - Mosa, Abu S.M.
N1 - Publisher Copyright:
©2022 AMIA - All rights reserved.
PY - 2022/5/23
Y1 - 2022/5/23
N2 - 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 (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%.
AB - 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 (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%.
UR - http://www.scopus.com/inward/record.url?scp=85134635806&partnerID=8YFLogxK
UR - https://pubmed.ncbi.nlm.nih.gov/35854719/
M3 - Article
C2 - 35854719
AN - SCOPUS:85134635806
VL - 2022
SP - 379
EP - 385
JO - AMIA ... Annual Symposium proceedings. AMIA Symposium
JF - AMIA ... Annual Symposium proceedings. AMIA Symposium
ER -