Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain.
Pneumology Service, Hospital Universitario Río Hortega, Valladolid, Spain.
PLoS One. 2018 Dec 7;13(12):e0208502. doi: 10.1371/journal.pone.0208502. eCollection 2018.
The gold standard for pediatric sleep apnea hypopnea syndrome (SAHS) is overnight polysomnography, which has several limitations. Thus, simplified diagnosis techniques become necessary.
The aim of this study is twofold: (i) to analyze the blood oxygen saturation (SpO2) signal from nocturnal oximetry by means of features from the wavelet transform in order to characterize pediatric SAHS; (ii) to evaluate the usefulness of the extracted features to assist in the detection of pediatric SAHS.
981 SpO2 signals from children ranging 2-13 years of age were used. Discrete wavelet transform (DWT) was employed due to its suitability to deal with non-stationary signals as well as the ability to analyze the SAHS-related low frequency components of the SpO2 signal with high resolution. In addition, 3% oxygen desaturation index (ODI3), statistical moments and power spectral density (PSD) features were computed. Fast correlation-based filter was applied to select a feature subset. This subset fed three classifiers (logistic regression, support vector machines (SVM), and multilayer perceptron) trained to determine the presence of moderate-to-severe pediatric SAHS (apnea-hypopnea index cutoff ≥ 5 events per hour).
The wavelet entropy and features computed in the D9 detail level of the DWT reached significant differences associated with the presence of SAHS. All the proposed classifiers fed with a selected feature subset composed of ODI3, statistical moments, PSD, and DWT features outperformed every single feature. SVM reached the highest performance. It achieved 84.0% accuracy (71.9% sensitivity, 91.1% specificity), outperforming state-of-the-art studies in the detection of moderate-to-severe SAHS using the SpO2 signal alone.
Wavelet analysis could be a reliable tool to analyze the oximetry signal in order to assist in the automated detection of moderate-to-severe pediatric SAHS. Hence, pediatric subjects suffering from moderate-to-severe SAHS could benefit from an accurate simplified screening test only using the SpO2 signal.
儿童睡眠呼吸暂停低通气综合征(SAHS)的金标准是夜间多导睡眠图,但它有几个局限性。因此,简化的诊断技术变得很有必要。
本研究旨在:(i)通过对儿童夜间血氧饱和度(SpO2)信号进行小波变换的特征分析,以对小儿 SAHS 进行特征描述;(ii)评估所提取特征对小儿 SAHS 检测的有效性。
共使用了 981 名年龄在 2-13 岁之间的儿童的 SpO2 信号。离散小波变换(DWT)因其适用于处理非平稳信号以及能够以高分辨率分析 SpO2 信号中与 SAHS 相关的低频成分而被选用。此外,计算了 3%氧减饱和度指数(ODI3)、统计矩和功率谱密度(PSD)特征。采用快速相关滤波器选择特征子集。该子集为三个分类器(逻辑回归、支持向量机(SVM)和多层感知器)提供输入,以确定是否存在中重度儿童 SAHS(呼吸暂停低通气指数截断值≥每小时 5 次事件)。
与 SAHS 的存在显著相关的是小波熵和在 DWT 的 D9 细节水平上计算的特征。所有基于所选择的特征子集的分类器,包括 ODI3、统计矩、PSD 和 DWT 特征,都优于单个特征。SVM 的性能最高,其准确率为 84.0%(71.9%的敏感性,91.1%的特异性),在使用 SpO2 信号单独检测中重度 SAHS 方面优于最先进的研究。
小波分析可能是一种可靠的工具,可以用来分析血氧饱和度信号,以帮助自动检测中重度小儿 SAHS。因此,患有中重度 SAHS 的儿科患者可能会受益于仅使用 SpO2 信号的准确简化筛查测试。