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

IWASAKI Ayako FUJIWARA Koichi 10642514 0000-0002-2929-0561 NAKAYAMA Chikao SUMI Yukiyoshi 10772923 0000-0001-6775-0883 KANO Manabu NAGAMOTO Tetsuharu KADOTANI Hiroshi 90362516 0000-0001-7474-3315 滋賀医科大学

2022.04.30

概要

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.

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

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

概要

この論文で使われている画像

関連論文

Screening of sleep apnea based on heart rate variability and long short-term memory.

Utilizing machine learning for detecting patients with IgA nephropathy from computerized medical bill database

Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data

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参考文献