Sleep Apnea Detection by Combining Long Short-Term Memory and Heart Rate Variability

Abstract

Sleep apnea syndrome (SAS) is a prevalent disorder which causes daytime fatigue with increased risk of cardiovascular diseases. A large number of patients are undiagnosed and untreated partly because of the difficulty in performing its gold standard test, polysomnography (PSG). In this research, we propose a simple screening method utilizing heart rate variability (HRV) and long short-term memory (LSTM) which is a kind of the neural network techniques. The result of applying this algorithm to clinical data demonstrates that it can discriminate between patients and healthy people with sensitivity (100%) and specificity (100%).