Pediatric Obstructive Sleep Apnea (OSA) is a respiratory disease whose diagnosis is performed through overnight polysomnography (PSG). Since it is a complex, time-consuming, expensive, and labor-intensive test, simpler alternatives are being intensively sought. In this study, bispectral analysis of overnight airflow (AF) signal is proposed as a potential approach to replace PSG when indicated. Thus, our objective was to characterize AF through bispectrum, and assess its performance to diagnose pediatric OSA. This characterization was conducted using 13 bispectral features from 946 AF signals. The oxygen desaturation index ≥3% (ODI3), a common clinical measure of OSA severity, was also obtained to evaluate its complementarity to the AF bispectral analysis. The fast correlation-based filter (FCBF) and a multi-layer perceptron (MLP) were used for subsequent automatic feature selection and pattern recognition stages. FCBF selected 3 bispectral features and ODI3, which were used to train an MLP model with ability to estimate apnea-hypopnea index (AHI). The model reached 82.16%, 82.49%, and 90.15% accuracies for the common AHI cut-offs 1, 5, and 10 events/h, respectively. The different bispectral approaches used to characterize AF in children provided complementary information. Accordingly, bispectral analysis showed that the occurrence of apneic events decreases the non-gaussianity and non-linear interaction of the AF harmonic components, as well as the regularity of the respiratory patterns. Moreover, the bispectral information from AF also showed complementarity with ODI3. Our findings suggest that AF bispectral analysis may serve as a useful tool to simplify the diagnosis of pediatric OSA, particularly for children with moderate-to-severe OSA.

小儿阻塞性睡眠呼吸暂停（OSA）是一种呼吸系统疾病，其诊断通过通宵多导睡眠图（PSG）进行。由于它是一种复杂，耗时，昂贵且劳动密集的测试，因此正在寻求更简单的替代方法。在这项研究中，建议对过夜气流（AF）信号进行双光谱分析，作为在指示时替代PSG的潜在方法。因此，我们的目标是通过双频谱来表征房颤，并评估其在诊断小儿OSA中的性能。使用来自946个AF信号的13个双光谱特征进行了表征。氧饱和度指数≥3％（ODI3），也获得了OSA严重程度的常见临床指标，以评估其与AF双频谱分析的互补性。基于快速相关性的滤波器（FCBF）和多层感知器（MLP）用于后续的自动特征选择和模式识别阶段。FCBF选择了3种双光谱特征和ODI3，用于训练具有估计呼吸暂停-呼吸不足指数（AHI）的能力的MLP模型。对于常见的AHI临界值1、5和10事件/小时，该模型的准确度分别达到82.16％，82.49％和90.15％。用于表征儿童房颤的不同双光谱方法提供了补充信息。因此，双光谱分析表明，呼吸暂停事件的发生降低了房颤谐波分量的非高斯性和非线性相互作用，以及呼吸模式的规律性。此外，AF的双谱信息也显示出与ODI 3的互补性。我们的发现表明，AF双谱分析可以作为简化小儿OSA诊断的有用工具，特别是对于中度至重度OSA的儿童。