Kulkas A, Huupponen E, Virkkala J, Tenhunen M, Saastamoinen A, Rauhala E, Himanen S-L
Department of Clinical Neurophysiology, Medical Imaging Center, Pirkanmaa Hospital District, Tampere, Finland.
Med Biol Eng Comput. 2009 Apr;47(4):405-12. doi: 10.1007/s11517-009-0446-z. Epub 2009 Feb 11.
Sleep apnoea syndrome is common in the general population and is currently underdiagnosed. The aim of the present work was to develop a new tracheal sound feature for separation of apnoea events from non-apnoea time. Ten overnight recordings from apnoea patients containing 1,107 visually scored apnoea events totalling 7 h in duration and 72 h of non-apnoea time were included in the study. The feature was designed to describe the local spectral content of the sound signal. The median, maximum and mean smoothing of different time scales were compared in the feature extraction. The feature was designed to range from 0 to 1 irrespective of tracheal sound amplitudes. This constant range could offer application of the feature without patient-specific adjustments. The overall separation of feature values during apnoea events from non-apnoea time across all patients was good, reaching 80.8%. Due to the individual differences in tracheal sound signal amplitudes, developing amplitude-independent means for screening apnoea events is beneficial.
睡眠呼吸暂停综合征在普通人群中很常见,目前存在诊断不足的情况。本研究的目的是开发一种新的气管声音特征,用于从非呼吸暂停时间中分离出呼吸暂停事件。该研究纳入了10名呼吸暂停患者的夜间记录,其中包含1107个经视觉评分的呼吸暂停事件,总时长为7小时,以及72小时的非呼吸暂停时间。该特征旨在描述声音信号的局部频谱内容。在特征提取过程中,比较了不同时间尺度的中位数、最大值和均值平滑处理。该特征的设计范围为0到1,与气管声音幅度无关。这个恒定范围可以在无需针对患者进行特定调整的情况下应用该特征。所有患者在呼吸暂停事件期间与非呼吸暂停时间的特征值总体分离效果良好,达到了80.8%。由于气管声音信号幅度存在个体差异,开发与幅度无关的方法来筛查呼吸暂停事件是有益的。