Faculty of Medicine, Kyoto University, Kyoto 606-8501, Japan.
Department of Systems Science, Kyoto University, Kyoto 606-8501, Japan; Department of Materials Process Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan.
Clin Neurophysiol. 2022 Jul;139:80-89. doi: 10.1016/j.clinph.2022.04.012. Epub 2022 Apr 30.
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.
易于检测出未确诊的睡眠呼吸暂停综合征（SAS）患者需要一种家庭使用的 SAS 筛查系统。在本研究中，我们使用大型临床多导睡眠图（PSG）数据集（N=938）验证了先前开发的 SAS 筛查方法。
我们结合了 R-R 间隔（RRI）和长短期记忆（LSTM），这是一种递归神经网络类型，并使用训练数据集（N=468）创建了一个用于区分呼吸状况的模型。使用验证数据集（N=470）验证其性能。
我们的方法筛查出严重 SAS 患者（呼吸暂停低通气指数；AHI≥30）的 AUC 为 0.92，灵敏度为 0.80，特异性为 0.84。此外，该模型筛查出中度/重度 SAS 患者（AHI≥15）的 AUC 为 0.89，灵敏度为 0.75，特异性为 0.87。
我们的方法在应用于大型临床数据集时取得了较高的筛查性能。
我们的方法可以帮助实现易于使用的 SAS 筛查系统，因为 RRI 数据可以通过可穿戴心率传感器轻松测量。它已经在包括具有各种背景的受试者的大型数据集上进行了验证，预计在实际临床实践中表现良好。
