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

•Sleep apnea syndrome (SAS) screening AI-based on R-R interval data was validated with a large clinical polysomnography dataset.•AUC of 0.92, a sensitivity of 0.80 and a specificity of 0.84 were achieved.•The SAS screening algorithm is easy to implement into a smartphone app. 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). 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). 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. Our method achieved high screening performance when applied to a large clinical dataset. 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.