A Sub-frame Based Feature Extraction Approach from Split-Band EEG Signal for Sleep Apnea Event Detection Using Multi-Layer LSTM

Abstract

Sleep apnea is a sleep disorder that millions of people all over the world are affected with. Untreated Sleep apnea can lead to various complex health issues including death. The detection of apnea events has been a pressing topic of research in the recent years. Several signals like polysomnography (PSG), electrocardiogram (ECG), electroencephalogram (EEG) are used to detect sleep apnea. In this paper, a novel approach has been proposed using EEG signal. The decomposed EEG signal is fed into a Long Short Term Memory (LSTM) model to explore the sequence of the signals. The output is then used for a Deep Neural Network (DNN) to correctly detect apnea frames. Lastly all the predictions are post-processed to get the final result. This scheme has the potential to be used in hospitals for continuous detection of sleep apnea event.