An ANN-Based Detection of Obstructive Sleep Apnea from Simultaneous ECG and SpO2 Recordings
Obstructive sleep apnea (OSA) is one of the most common sleep disorders characterized by a disruption of breathing during sleep. This disease, though common, goes undiagnosed in most cases because of the inconvenience, cost, and/or unavailability of opting for polysomnography (PSG) and a sleep analyst. Many researchers are working on devising an unsupervised, cost-effective, and convenient OSA detection methods which will aid the timely diagnosis of this sleep disorder. Commonly used signals to detect OSA are ECG, EEG, pulse oximetry (SpO2), blood oxygen saturation (SaO2), and heart rate variability (HRV). In this work, an attempt to detect the OSA using simultaneously acquired ECG and SpO2 signals has been presented. Various features from the RR intervals of ECG, and a couple of features—namely, CT90 and delta index—from the SpO2, were extracted as indicators of OSA. The features were then fed to a trained artificial neural network (ANN) which classified the signals as OSA positive or OSA negative. The proposed technique boasts a very high accuracy of 98.3%, which is superior to other competing techniques reported so far.
KeywordsSleep apnea ECG SpO2 Machine learning ANN
We thank our institute, Fr. Conceicao Rodrigues College of Engineering, for providing all possible supports to this work.
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