Kaniusas Eugenijus, Pfützner Helmut, Saletu Bernd
Institute of Fundamentals and Theory of Electrical Engineering, Bioelectricity and Magnetism Laboratory, University of Technology, Vienna, Austria.
IEEE Trans Biomed Eng. 2005 Nov;52(11):1812-22. doi: 10.1109/TBME.2005.856294.
Traditionally, auscultation is applied to the diagnosis of either respiratory disturbances by respiratory sounds or cardiac disturbances by cardiac sounds. In addition, for sleep apnea syndrome diagnosis, snoring sounds are also monitored. The present study was aimed at synchronous detection of all three sound components (cardiac, respiratory, and snoring) from a single spot. The sounds were analyzed with respect to the cardiorespiratory activity, and to the detection and classification of apneas. Sound signals from 30 subjects including 10 apnea patients were detected by means of a microphone connected to a chestpiece which was applied to the heart region. The complex nature of the signal was investigated using time, spectral, and statistical approaches, in connection with self-defined time-based and event-based characteristics. The results show that the obstruction is accompanied by an increase of statistically relevant spectral components in the range of 300 to 2000 Hz, however, not within the range up to 300 Hz. Signal properties are discussed with respect to different breathing types, as well as to the presence and the type of apneas. Principal component analysis of the event-based characteristics shows significant properties of the sound signal with respect to different types of apneas and different patient groups, respectively. The analysis reflects apneas with an obstructive segment and those with a central segment. In addition, aiming for an optimum detection of all three sound components, alternative regions on the thorax and on the neck were investigated on two subjects. The results suggest that the right thorax region in the seventh intercostal space and the neck are optimal regions. It is concluded that for patient assessment, extensive acoustic analysis offers a reduction in the number of required sensor components, especially with respect to compact home monitoring of apneas.
传统上,听诊用于通过呼吸音诊断呼吸紊乱或通过心音诊断心脏紊乱。此外,对于睡眠呼吸暂停综合征的诊断,还会监测打鼾声。本研究旨在从单个部位同步检测所有三种声音成分(心脏、呼吸和打鼾)。对这些声音进行了心肺活动分析以及呼吸暂停的检测和分类。通过连接到置于心脏区域的胸件的麦克风,检测了包括10名呼吸暂停患者在内的30名受试者的声音信号。结合自定义的基于时间和基于事件的特征,使用时间、频谱和统计方法研究了信号的复杂性质。结果表明,阻塞伴随着300至2000Hz范围内统计相关频谱成分的增加,然而,在高达300Hz的范围内则没有。针对不同的呼吸类型以及呼吸暂停的存在和类型,讨论了信号特性。基于事件特征的主成分分析分别显示了声音信号在不同类型呼吸暂停和不同患者组方面的显著特性。该分析反映了具有阻塞段的呼吸暂停和具有中枢段的呼吸暂停。此外,为了实现对所有三种声音成分的最佳检测,在两名受试者身上研究了胸部和颈部的其他区域。结果表明,第七肋间间隙的右胸区域和颈部是最佳区域。得出的结论是,对于患者评估,广泛的声学分析可以减少所需传感器组件的数量,特别是对于呼吸暂停的紧凑型家庭监测。