A RR interval based automated apnea detection approach using residual network. (July 2019)

• Record Type: Journal Article
• Authors: Wang, Lei; Lin, Youfang; Wang, Jing
• Is Part Of: Computer methods and programs in biomedicine. Volume 176(2019)
• Page Start: 93
• Page End: 104
• Publication Date: 2019-07
• Subjects: Sleep apnea -- Electrocardiogram (ECG) -- RR Interval -- Residual network -- Deep learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.05.002
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Method achieved a good result of 94.4% accuracy for apnea detection in segment. Firstly employ deep residual network for apnea detectionbased on low-sample-rate data. Weights of network can be fine-tuned on clinical. Strong adaptivity. Model also works well using ECG-derived respiration (EDR) signal. Abstract: Background and Objective: Apnea is one of the most common conditions that causes sleep-disorder breathing. With growing number of patients worldwide, more and more patients suffer from complications of apnea. But most of them stay untreated due to the complex and time-consuming polysomnography (PSG) diagnosis method. Effective and precise diagnosis support system using electrocardiograph (ECG) is required. In this paper, we propose an approach using residual network to detect apnea based on RR intervals (intervals between R-peaks of ECG signal). Methods: In our model, we apply residual network to represent information carried by RR intervals. Moreover, we proposed a novel perspective, called dynamic autoregressive representation, to provide interpretation of representing the RR intervals by convolutional layers. Results: This approach is tested for per-segment apnea detection using publicly available dataset on Physionet. 30 overnight recordings are used for training and 5 for testing. We achieve a good result of 94.4% accuracy, 93.0% sensitivity and 94.9% specificity. This result outperform other prevalent methods based on RR intervals. This model also shows its good adaptivity while using ECG-derived respiration signal (EDR) in experiments. Its extensiveness is evaluated and compared in experiments. The proposed model is also compared with deep neural networks using original ECG signals for apnea detection, and it achieves better result using fewer input samples. Conclusions: We develop a deep residual network to detect apnea on low-sample-rate RR intervals. The result suggests a possibility of representing RR intervals by neural network. The model showed strong adaptivity when using EDR input.