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信号之一，尤其是在睡眠呼吸暂停患者中，是气流。一种估算气流的简便方法是基于对气管声音和运动的分析。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。纳入实验室睡眠研究的疑似睡眠呼吸暂停成人的数据。与多导睡眠图同时，使用连接到胸骨上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相位。然后，对于每个阶段，一组来自声能和气管运动的形态特征被提取出来，以将局部阶段分为吸气或呼气。正常呼吸时检测呼吸相位的平均误差和时间延迟分别为7.62%和181 ms，打鼾时为8.95%和194 ms，呼吸事件时分别为13.19%和220 ms。吸气的平均分类准确率为 83.7%，呼气的平均分类准确率为 75.0%。从睡眠期间的气管声音和运动准确识别呼吸阶段。吸气的平均分类准确率为 83.7%，呼气的平均分类准确率为 75.0%。从睡眠期间的气管声音和运动准确识别呼吸阶段。吸气的平均分类准确率为 83.7%，呼气的平均分类准确率为 75.0%。从睡眠期间的气管声音和运动准确识别呼吸阶段。