Real-Time Obstructive Sleep Apnea Detection from Raw ECG and SpO2 Signal Using Convolutional Neural Network.

Abstract

Obstructive sleep apnea is a sleep disorder that is linked with many health complications and severe form of apnea can even be lethal. Overnight polysomnography is the gold standard for diagnosing apnea, which is expensive, time-consuming, and requires manual analysis by a sleep expert. Recently, there have been numerous studies demonstrating the application of artificial intelligence to detect apnea in real time. But the majority of these studies apply data pre-processing and feature extraction techniques resulting in a longer inference time that makes the real-time detection system inefficient. This study proposes a single convolutional neural network architecture that can automatically extract spatial features and detect apnea from both electrocardiogram (ECG) and blood-oxygen saturation (SpO₂) signals. Using segments of 10s, the network classified apnea with an accuracy of 94.2% and 96% for ECG and SpO₂ respectively. Moreover, the overall performance of both models was consistent with an AUC score of 0.99.