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利用鼾声和动脉血氧饱和度对睡眠呼吸障碍进行数字监测。

Digital monitoring of sleep-disordered breathing using snoring sound and arterial oxygen saturation.

作者信息

Issa F G, Morrison D, Hadjuk E, Iyer A, Feroah T, Remmers J E

机构信息

Alberta Lung Association Sleep Disorders Center, Foothills Hospital, Calgary, Canada.

出版信息

Am Rev Respir Dis. 1993 Oct;148(4 Pt 1):1023-9. doi: 10.1164/ajrccm/148.4_Pt_1.1023.

Abstract

A new portable digital recorder (SNORESAT) that uses the sound of snoring and arterial oxygen saturation (SaO2) to monitor breathing abnormalities during sleep was constructed and compared in the laboratory with standard overnight polysomnography (PSG). The device digitally records sound from a transducer applied to the chest and SaO2 from a commercially available ear oximeter. A snore is identified when the moving time average of the sound exceeds a threshold voltage level longer than 0.26 s. The stored data are transferred to a personal computer for poststudy analysis. An analysis algorithm identifies a respiratory disturbance event when a quiet period of 10 to 120 s separates two snores and is associated with a fall in SaO2 exceeding 3%. The respiratory disturbance index (RDI), mean apnea duration, mean lowest SaO2, and number of desaturations > 3% are computed. A total of 129 referrals to the sleep apnea outpatient clinic underwent simultaneous all-night recording of PSG and SNORESAT. Using the computed RDI recorded by the SNORESAT, the sensitivity and specificity of the monitor in detecting sleep apnea syndrome (SAS) ranged between 84 and 90% and 95 and 98%, respectively, depending on the PSG value of RDI used to define SAS (range, > or = 7 to > or = 20 events/h). Using a PSG value of RDI > or = 10, or > or = 20 RD/h as the definition for SAS, the prevalence of SAS in the referral population was 45 and 31%, respectively.(ABSTRACT TRUNCATED AT 250 WORDS)

摘要

一种新型便携式数字记录仪(SNORESAT)被制造出来,它利用鼾声和动脉血氧饱和度(SaO2)来监测睡眠期间的呼吸异常情况,并在实验室中与标准的夜间多导睡眠图(PSG)进行了比较。该设备通过应用于胸部的传感器数字记录声音,并通过市售的耳部血氧仪记录SaO2。当声音的移动时间平均值超过阈值电压水平且持续时间超过0.26秒时,即可识别出鼾声。存储的数据被传输到个人计算机进行研究后分析。当两个鼾声之间有10至120秒的安静期且与SaO2下降超过3%相关联时,分析算法会识别出呼吸紊乱事件。计算呼吸紊乱指数(RDI)、平均呼吸暂停持续时间、平均最低SaO2以及饱和度下降>3%的次数。共有129名转诊至睡眠呼吸暂停门诊的患者同时进行了PSG和SNORESAT的整夜记录。根据用于定义睡眠呼吸暂停综合征(SAS)的PSG的RDI值(范围为≥7至≥20次事件/小时),使用SNORESAT记录的计算出的RDI,该监测仪检测SAS的敏感性和特异性分别在84%至90%和95%至98%之间。以PSG的RDI≥10或≥20次呼吸紊乱/小时作为SAS的定义,转诊人群中SAS的患病率分别为45%和31%。(摘要截断于250字)

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