Prediction of Sleep Apnea Occurrence from a Single-Lead Electrocardiogram Using Stacking Hybrid Architecture with Gated Recurrent Neural Network Architectures and Logistic Regression

Obstructive sleep apnea (OSA) is a common sleep disorder that impacts patient health and imposes a burden on families and healthcare systems. The diagnosis of OSA is usually performed through overnight polysomnography (PSG) in a hospital setting. In recent years, OSA detection using a single-lead electrocardiogram (ECG) has been explored. The advantage of this method is that patients can be measured in home environments. Thus, the aim of this study was to predict occurrences of sleep apnea with parameters extracted from previous single-lead ECG measurements. The parameters were the R-R interval (RRI) and R-wave amplitude (RwA). The dataset was the single-lead ECG Apnea-ECG Database, and a stacking hybrid architecture (SHA) including three gated recurrent neural network architectures (GRNNAs) and logistic regression was proposed to improve the accuracy of OSA detection. Three GRNNAs used three different recurrent neural networks: Bidirectional Long Short-Term Memory (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). The challenge of this method was in exploring how many minutes of previous RRI and RwA measurements (n minutes) have the best performance in predicting occurrences of sleep apnea in the future (h minutes). The results showed that the SHA under an n of 20 min had the best performance in predicting occurrences of sleep apnea in the following 10 min: the SHA achieved a precision of 95.79%, sensitivity of 94.74%, specificity of 97.48%, F1-score of 95.26%, and accuracy of 96.45%. The proposed SHA was successful in predicting future sleep apnea occurrence with a single-lead ECG. Thus, this approach could be used in the development of wearable sleep monitors for the management of sleep apnea.