Sola-Soler Jordi, Fiz Jose A, Torres Abel, Jane Raimon
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:4232-5. doi: 10.1109/EMBC.2014.6944558.
Obstructive Sleep Apnea (OSA) is currently diagnosed by a full nocturnal polysomnography (PSG), a very expensive and time-consuming method. In previous studies we were able to distinguish patients with OSA through formant frequencies of breath sound during sleep. In this study we aimed at identifying OSA patients from breath sound analysis during wakefulness. The respiratory sound was acquired by a tracheal microphone simultaneously to PSG recordings. We selected several cycles of consecutive inspiration and exhalation episodes in 10 mild-moderate (AHI<;30) and 13 severe (AHI>=30) OSA patients during their wake state before getting asleep. Each episode's formant frequencies were estimated by linear predictive coding. We studied several formant features, as well as their variability, in consecutive inspiration and exhalation episodes. In most subjects formant frequencies were similar during inspiration and exhalation. Formant features in some specific frequency band were significantly different in mild OSA as compared to severe OSA patients, and showed a decreasing correlation with OSA severity. These formant characteristics, in combination with some anthropometric measures, allowed the classification of OSA subjects between mild-moderate and severe groups with sensitivity (specificity) up to 88.9% (84.6%) and accuracy up to 86.4%. In conclusion, the information provided by formant frequencies of tracheal breath sound recorded during wakefulness may allow identifying subjects with severe OSA.
阻塞性睡眠呼吸暂停（OSA）目前通过全夜多导睡眠图（PSG）进行诊断，这是一种非常昂贵且耗时的方法。在先前的研究中，我们能够通过睡眠期间呼吸音的共振峰频率来区分OSA患者。在本研究中，我们旨在通过清醒期间的呼吸音分析来识别OSA患者。呼吸音由气管麦克风在进行PSG记录的同时采集。我们在10名轻度至中度（呼吸暂停低通气指数<30）和13名重度（呼吸暂停低通气指数≥30）OSA患者入睡之前的清醒状态下，选取了几个连续吸气和呼气阶段的周期。每个阶段的共振峰频率通过线性预测编码进行估计。我们研究了连续吸气和呼气阶段的几个共振峰特征及其变异性。在大多数受试者中，吸气和呼气期间的共振峰频率相似。与重度OSA患者相比，轻度OSA患者在某些特定频段的共振峰特征存在显著差异，并且与OSA严重程度的相关性呈下降趋势。这些共振峰特征与一些人体测量指标相结合，能够将OSA受试者分为轻度至中度和重度两组，灵敏度（特异度）高达88.9%（84.6%），准确率高达86.4%。总之，清醒期间记录的气管呼吸音共振峰频率所提供的信息可能有助于识别重度OSA患者。
