Marcos J Victor, Hornero Roberto, Alvarez Daniel, Del Campo Félix, López Miguel
Biomedical Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, Camino del Cementerio s/n, 47011-Valladolid, Spain.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:5174-7. doi: 10.1109/IEMBS.2007.4353507.
The aim of this study was to assess the ability of neural networks as an assistant tool for the diagnosis of the obstructive sleep apnea syndrome (OSAS). A total of 187 subjects suspected of suffering from OSAS (111 with a positive diagnosis of OSAS and 76 with a negative diagnosis of OSAS) took part in the study. The initial population was divided into training, validation and test sets for deriving and testing our neural classifiers. Our method was based on spectral and non-linear features extracted from overnight arterial oxygen saturation (SaO_(2)) recordings. A seven-element input vector was used for patient classification. We selected four spectral features from the estimated power spectral density (PSD) of SaO_(2). In addition, three input features were computed from non-linear analysis of SaO_(2). Two neural classifiers were assessed: the multilayer perceptron (MLP) network and the radial basis function (RBF) network. The RBF classifier provided the best diagnostic performance with an accuracy of 86.3% (89.9% sensitivity and 81.1% specificity).
本研究的目的是评估神经网络作为诊断阻塞性睡眠呼吸暂停综合征（OSAS）辅助工具的能力。共有187名疑似患有OSAS的受试者（111名OSAS诊断阳性，76名OSAS诊断阴性）参与了该研究。最初的人群被分为训练集、验证集和测试集，用于推导和测试我们的神经分类器。我们的方法基于从夜间动脉血氧饱和度（SaO₂）记录中提取的频谱和非线性特征。一个七元素输入向量用于患者分类。我们从估计的SaO₂功率谱密度（PSD）中选择了四个频谱特征。此外，从SaO₂的非线性分析中计算出三个输入特征。评估了两种神经分类器：多层感知器（MLP）网络和径向基函数（RBF）网络。RBF分类器提供了最佳的诊断性能，准确率为86.3%（灵敏度为89.9%，特异性为81.1%）。
