Gutierrez-Tobal Gonzalo C, Kheirandish-Gozal Leila, Vaquerizo-Villar Fernando, Alvarez Daniel, Barroso-Garcia Veronica, Crespo Andrea, Campo Felix Del, Gozal David, Hornero Roberto
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:175-178. doi: 10.1109/EMBC.2018.8512248.
This study aims at assessing the bispectral analysis of blood oxygen saturation (SpO) from nocturnal oximetry to help in pediatric sleep apnea-hypopnea syndrome (SAHS) diagnosis. Recent studies have found excessive redundancy in the SAHS-related information usually extracted from SpO, while proposing only two features as a reduced set to be used. On the other hand, it has been suggested that SpO bispectral analysis is able to provide complementary information to common anthropometric, spectral, and clinical variables. We address these novel findings to assess whether bispectrum provides new non-redundant information to help in SAHS diagnosis. Thus, we use 981 pediatric SpO recordings to extract both the reduced set of features recently proposed as well as 9 bispectral features. Then, a feature selection method based on the fast correlationbased filter and bootstrapping is used to assess redundancy among all the features. Finally, the non-redundant ones are used to train a Bayesian multi-layer perceptron neural network (BYMLP) that estimate the apnea-hypopnea index (AHI), which is the diagnostic reference variable. Bispectral phase entropy was found complementary to the two previously recommended features and a BY-MLP model trained with the three of them reached high agreement with actual AHI (intra-class correlation coefficient = 0.889). Estimated AHI also showed high diagnostic ability, reaching 82.1%, 81.9%, and 90.3% accuracies and 0.814, 0.880, and 0.922 area under the receiver-operating characteristics curve for three common AHI thresholds: 1 e/h, 5 e/h, and 10 e/h, respectively. These results suggest that the information extracted from the bispectrum of SpO can improve the diagnostic performance of the oximetry test.
本研究旨在评估夜间血氧饱和度（SpO）的双谱分析，以辅助小儿睡眠呼吸暂停低通气综合征（SAHS）的诊断。近期研究发现，通常从SpO中提取的与SAHS相关的信息存在过多冗余，同时仅提出两个特征作为简化集以供使用。另一方面，有人提出SpO双谱分析能够为常见的人体测量、频谱和临床变量提供补充信息。我们探讨这些新发现，以评估双谱是否能提供新的非冗余信息来辅助SAHS诊断。因此，我们使用981份小儿SpO记录来提取最近提出的简化特征集以及9个双谱特征。然后，采用基于快速相关滤波器和自抽样法的特征选择方法来评估所有特征之间的冗余性。最后，使用非冗余特征训练贝叶斯多层感知器神经网络（BYMLP），该网络用于估计呼吸暂停低通气指数（AHI），AHI是诊断参考变量。发现双谱相位熵与之前推荐的两个特征互补，使用这三个特征训练的BY-MLP模型与实际AHI高度一致（组内相关系数 = 0.889）。估计的AHI也显示出较高的诊断能力，对于三个常见的AHI阈值：1次/小时、5次/小时和10次/小时，其准确率分别达到82.1%、81.9%和90.3%，受试者工作特征曲线下面积分别为0.814、0.880和0.922。这些结果表明，从SpO双谱中提取的信息可以提高血氧测定测试的诊断性能。
