Alvarez Daniel, Kheirandish-Gozal Leila, Gutierrez-Tobal Gonzalo C, Crespo Andrea, Philby Mona F, Mohammadi Meelad, Del Campo Felix, Gozal David, Hornero Roberto
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:2800-3. doi: 10.1109/EMBC.2015.7318973.
Childhood obstructive sleep apnea-hypopnea syndrome (OSAHS) is a highly prevalent condition that negatively affects health, performance and quality of life of infants and young children. Early detection and treatment improves neuropsychological and cognitive deficits linked with the disease. The aim of this study was to assess the performance of automated analysis of blood oxygen saturation (SpO2) recordings as a screening tool for OSAHS. As an initial step, statistical, spectral and nonlinear features were estimated to compose an initial feature set. Then, fast correlation-based filter (FCBF) was applied to search for the optimum subset. Finally, the discrimination power (OSAHS negative vs. OSAHS positive) of three pattern recognition algorithms was assessed: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and logistic regression (LR). Three clinical cutoff points commonly used in the literature for positive diagnosis of the disease were applied: apnea-hypopnea index (AHI) of 1, 3 and 5 events per hour (e/h). Our methodology reached 88.6% accuracy (71.4% sensitivity and 100.0% specificity, 100.0% positive predictive value, and 84.0% negative predictive value) in an independent test set using QDA for a clinical cut-off point of 5 e/h. These results suggest that SpO2 nocturnal recordings may be used to develop a reliable and efficient screening tool for childhood OSAHS.
儿童阻塞性睡眠呼吸暂停低通气综合征（OSAHS）是一种高度常见的病症，会对婴幼儿的健康、表现及生活质量产生负面影响。早期检测和治疗可改善与该疾病相关的神经心理和认知缺陷。本研究的目的是评估自动分析血氧饱和度（SpO2）记录作为OSAHS筛查工具的性能。作为第一步，估计统计、频谱和非线性特征以组成初始特征集。然后，应用基于快速相关性的滤波器（FCBF）来搜索最优子集。最后，评估三种模式识别算法的判别能力（OSAHS阴性与OSAHS阳性）：线性判别分析（LDA）、二次判别分析（QDA）和逻辑回归（LR）。应用了文献中常用于该疾病阳性诊断的三个临床截断点：每小时呼吸暂停低通气指数（AHI）为1、3和5次事件（e/h）。在使用QDA且临床截断点为5 e/h的独立测试集中，我们的方法达到了88.6%的准确率（71.4%的灵敏度、100.0%的特异性、100.0%的阳性预测值和84.0%的阴性预测值）。这些结果表明，夜间SpO2记录可用于开发一种可靠且高效的儿童OSAHS筛查工具。
