Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, China.
J Clin Sleep Med. 2021 Sep 1;17(9):1777-1784. doi: 10.5664/jcsm.9292.
The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated.
Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models.
A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models.
Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events.
Wang B, Yi X, Gao J, et al. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. 2021;17(9):1777-1784.
本研究旨在检查预呼吸性打鼾声音的声学特性和睡眠特征，并探讨通过打鼾声音预测即将发生的呼吸事件的可行性。
连续招募习惯性打鼾或睡眠中呼吸声沉重的参与者。同时进行多导睡眠图检查和与打鼾相关的呼吸声记录。从 30 秒样本中提取声学特征和睡眠特征，并使用机器学习算法建立 2 个预测模型。
共纳入 74 名符合条件的参与者。通过 5 折交叉验证测试的模型 1，对呼吸事件的预测准确率为 0.92，曲线下面积为 0.94。通过留一法交叉验证测试的模型 2，具有声学特征和睡眠信息，准确率为 0.78，曲线下面积为 0.80。在所有对 2 个模型性能有贡献的睡眠特征中，睡眠姿势被发现是最重要的。
预呼吸性声音具有独特的声学特征，与打鼾相关的呼吸声可作为实时呼吸暂停事件预测器。这些模型结合睡眠信息，可作为预测呼吸暂停事件的预警系统的有前途的工具。
Wang B, Yi X, Gao J, et al. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. 2021;17(9):1777-1784.
