Kite - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.
Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
Ann Biomed Eng. 2021 Jun;49(6):1521-1533. doi: 10.1007/s10439-020-02651-5. Epub 2021 Jan 5.
One of the most important signals to assess respiratory function, especially in patients with sleep apnea, is airflow. A convenient method to estimate airflow is based on analyzing tracheal sounds and movements. However, this method requires accurate identification of respiratory phases. Our goal is to develop an automatic algorithm to analyze tracheal sounds and movements to identify respiratory phases during sleep. Data from adults with suspected sleep apnea who were referred for in-laboratory sleep studies were included. Simultaneously with polysomnography, tracheal sounds and movements were recorded with a small wearable device attached to the suprasternal notch. First, an adaptive detection algorithm was developed to localize the respiratory phases in tracheal sounds. Then, for each phase, a set of morphological features from sound energy and tracheal movement were extracted to classify the localized phases into inspirations or expirations. The average error and time delay of detecting respiratory phases were 7.62% and 181 ms during normal breathing, 8.95% and 194 ms during snoring, and 13.19% and 220 ms during respiratory events, respectively. The average classification accuracy was 83.7% for inspirations and 75.0% for expirations. Respiratory phases were accurately identified from tracheal sounds and movements during sleep.
评估呼吸功能的最重要的信号之一，特别是在睡眠呼吸暂停患者中，是气流。估计气流的一种方便方法是基于分析气管声音和运动。然而，这种方法需要准确识别呼吸阶段。我们的目标是开发一种自动算法来分析气管声音和运动，以识别睡眠期间的呼吸阶段。该研究纳入了疑似睡眠呼吸暂停并被转介进行实验室睡眠研究的成年人的数据。同时进行多导睡眠图监测，并使用贴在上胸骨切迹的小型可穿戴设备记录气管声音和运动。首先，开发了一种自适应检测算法来定位气管声音中的呼吸阶段。然后，对于每个阶段，从声音能量和气管运动中提取一组形态特征，将定位的阶段分类为吸气或呼气。在正常呼吸、打鼾和呼吸事件期间，检测呼吸阶段的平均误差和时间延迟分别为 7.62%和 181ms、8.95%和 194ms 以及 13.19%和 220ms。吸气的平均分类准确率为 83.7%，呼气的为 75.0%。成功地从睡眠期间的气管声音和运动中准确识别呼吸阶段。
