Automatic Analysis of Sleep Apnea using DNN andHRV features

The absence of oxygen supply to the human brain and body a breathing disorder that takes place during sleeping time is termed sleep apnea. The recommended approach was assessed utilizing the apnea-ECG dataset. The heart rate variability (HRV) features were applied to classify sleep apnea episodes. The Deep learning-based Neural Network (DNN) classifier was initiated to classify the Normal (N) and Apnea (A)episodes. A fusion of the time, frequency, and heartbeat interval features with the DNN classifier recorded 99.09% accuracy for detecting normal (N) and Apnea (A) episodes.