R-R interval-based sleep apnea screening by a recurrent neural network in a large clinical polysomnography dataset

Objective: Easily detecting patients with undiagnosed sleep apnea syndrome (SAS) requires a home-use SAS screening system. In this study, we validate a previously developed SAS screening methodology using a large clinical polysomnography (PSG) dataset (N = 938). Methods: We combined R-R interval (RRI) and long short-term memory (LSTM), a type of recurrent neural networks, and created a model to discriminate respiratory conditions using the training dataset (N = 468). Its performance was validated using the validation dataset (N = 470). Results: Our method screened patients with severe SAS (apnea hypopnea index; AHI > 30) with an area under the curve (AUC) of 0.92, a sensitivity of 0.80, and a specificity of 0.84. In addition, the model screened patients with moderate/severe SAS (AHI > 15) with an AUC of 0.89, a sensitivity of 0.75, and a specificity of 0.87. Conclusions: Our method achieved high screening performance when applied to a large clinical dataset. Significance: Our method can help realize an easy-to-use SAS screening system because RRI data can be easily measured with a wearable heart rate sensor. It has been validated on a large dataset including subjects with various backgrounds and is expected to perform well in real-world clinical practice. (c) 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study validates a SAS screening methodology using R-R interval and long short-term memory technology, achieving high screening performance in a large clinical dataset. The method can contribute to the realization of an easy-to-use SAS screening system.