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

Tanmoy Paul, Omiya Hassan, Khuder Alaboud, Humayera Islam, Md Kamruz Zaman Rana, Syed K. Islam, Abu S.M. Mosa

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

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%.

Original languageEnglish
Pages (from-to)379-385
Number of pages7
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2022
StatePublished - 23 May 2022

EGS Disciplines

  • Electrical and Computer Engineering

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