Recurrent Neural Network Based Classification of ECG Signal Features for Obstruction of Sleep Apnea Detection

摘要

This paper introduces an OSA detection method based on Recurrent Neural network. At the first step, RR interval (time interval from one R wave to the next R wave) is employed to extract the signals from Apnea- Electrocardiogram (ECG) where all extracted features are then used as an input for the designed deep model. Then an architecture having four recurrent layers and batch normalization layers are designed and trained with the extracted features for OSA detection. Apnea-ECG datasets from physionet.org are used for training and testing our model. Experimental results reveal that our automatic OSA detection model provides better classification accuracy.