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整夜分析睡眠呼吸暂停低通气综合征患者的鼾声间隔时间。

All night analysis of time interval between snores in subjects with sleep apnea hypopnea syndrome.

机构信息

Department ESAII, Universitat Politècnica de Catalunya, Barcelona, Spain.

出版信息

Med Biol Eng Comput. 2012 Apr;50(4):373-81. doi: 10.1007/s11517-012-0885-9. Epub 2012 Mar 10.

Abstract

Sleep apnea-hypopnea syndrome (SAHS) is a serious sleep disorder, and snoring is one of its earliest and most consistent symptoms. We propose a new methodology for identifying two distinct types of snores: the so-called non-regular and regular snores. Respiratory sound signals from 34 subjects with different ranges of Apnea-Hypopnea Index (AHI = 3.7-109.9 h(-1)) were acquired. A total number of 74,439 snores were examined. The time interval between regular snores in short segments of the all night recordings was analyzed. Severe SAHS subjects show a shorter time interval between regular snores (p = 0.0036, AHI cp: 30 h(-1)) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p = 0.006, AHI cp: 30 h(-1)) is seen for less severe SAHS subjects. Features derived from the analysis of time interval between regular snores achieved classification accuracies of 88.2 % (with 90 % sensitivity, 75 % specificity) and 94.1 % (with 94.4 % sensitivity, 93.8 % specificity) for AHI cut-points of severity of 5 and 30 h(-1), respectively. The features proved to be reliable predictors of the subjects' SAHS severity. Our proposed method, the analysis of time interval between snores, provides promising results and puts forward a valuable aid for the early screening of subjects suspected of having SAHS.

摘要

睡眠呼吸暂停低通气综合征(SAHS)是一种严重的睡眠障碍,而打鼾是其最早和最一致的症状之一。我们提出了一种新的方法来识别两种不同类型的鼾声:所谓的不规则和规则鼾声。从 34 名呼吸暂停低通气指数(AHI=3.7-109.9 h(-1))范围不同的受试者中采集了呼吸声信号。共检查了 74439 次打鼾。分析了整夜记录中短片段中规则打鼾之间的时间间隔。严重 SAHS 患者的规则打鼾之间的时间间隔更短(p=0.0036,AHI cp:30 h(-1)),并且在所有睡眠期间时间间隔特征的离散度更小。相反,对于较轻的 SAHS 患者,会看到较低的段内变异性(p=0.006,AHI cp:30 h(-1))。规则打鼾之间的时间间隔分析得出的特征,对于严重程度的 AHI 切点为 5 和 30 h(-1),分类准确率分别为 88.2%(敏感性为 90%,特异性为 75%)和 94.1%(敏感性为 94.4%,特异性为 93.8%)。这些特征被证明是受试者 SAHS 严重程度的可靠预测指标。我们提出的方法,即打鼾之间时间间隔的分析,提供了有希望的结果,并为疑似患有 SAHS 的患者的早期筛查提出了有价值的帮助。

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