@article{oai:shiga-med.repo.nii.ac.jp:00004218,
 author = {角, 幸頼 and KADOTANI, Hiroshi and 角谷, 寛 and IWASAKI, Ayako and FUJIWARA, Koichi and NAKAYAMA, Chikao and SUMI, Yukiyoshi and KANO, Manabu and NAGAMOTO, Tetsuharu and KADOTANI, Hiroshi and 角谷, 寛},
 journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
 month = {Apr},
 note = {pdf, 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., Journal Article},
 pages = {80--89},
 title = {R-R interval-based sleep apnea screening by a recurrent neural network in a large clinical polysomnography dataset.},
 volume = {139},
 year = {2022},
 yomi = {カドタニ, ヒロシ and カドタニ, ヒロシ}
}