Automatic IHR-based sleep stage detection using features of residual neural network

Abstract

Untreated sleep disorders can harm bodily functions, and a sleep study and monitoring of sleep stages are the first steps in diagnosing these disorders. Using Polysomnography (PSG), signal scoring for sleep stage determination has become a familiar investigation in recent years. Despite its effectiveness, the procedure is time-consuming and costly. This study presents a cost-effective method for sleep classification based on Electrocardiogram (ECG) input signals. We proposed a multi-ethnic study of the Atherosclerosis dataset, including 1700 PSG, to develop a Residual Neural Network (RNN) classifier to stage sleep from Instantaneous Heart Rate (IHR) extracted from the ECG signals. The proposed system follows the following steps: ECG collection, signal preprocessing (including ECG normalization and segmentation, instant heart rate calculation and normalization, resampling, and filtering), and classification using an RNN. A Convolutional Neural Network (CNN) is used to detect sleep stages using preprocessed segments of the IHR time series of 240 samples centered on 30-s epochs as inputs. The proposed algorithm in the five-fold cross-validation achieved an accuracy of 85.32%, a kappa of 77.11%, a Sensitivity of 81.14%, a Specificity of 82.68%, and an F-1 score of 81.87%. The results show that ECG data provide valuable information about sleep stages for a large population.