MacGregor Cameron A, Moussavi Zahra
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:4236-9. doi: 10.1109/EMBC.2014.6944559.
Obstructive sleep apnea (OSA) is a widespread disorder that is cumbersome to diagnose using the goldstandard, overnight polysomnography (PSG). This paper highlights further development of our Awake-OSA method for predicting whether someone has severe sleep apnea using breath sounds recorded during wakefulness. We propose the use of an expert classification approach that consists of individual majority-voting classifiers. Each classifier is trained to distinguish one class of subject from all other classes. The outcomes of these classifiers are, in turn, combined using a truth matrix to determine the final outcome. Using the breath sound features of 249 subjects, the classifiers attempted to classify 180 subjects as either non-OSA (AHI less than 5) or severe-OSA (AHI greater than 30). 79% and 75% of OSA and non-OSA subjects, respectively, could be classified. Of those classified, the resultant testing sensitivity and specificity were found to be 78% and 86%, respectively. The consistency of the testing to training accuracies indicates the robustness and generalizability of using multiple expert classifiers on the dataset. This technique has the potential to be used in a doctor's office to rapidly and cheaply pre-screen for OSA, so that physicians may be better able to determine which patients are in need of overnight PSG.
阻塞性睡眠呼吸暂停（OSA）是一种普遍存在的疾病，使用金标准——夜间多导睡眠图（PSG）进行诊断很麻烦。本文重点介绍了我们的清醒时OSA方法的进一步发展，该方法利用清醒期间记录的呼吸音来预测某人是否患有严重睡眠呼吸暂停。我们建议使用一种专家分类方法，该方法由个体多数投票分类器组成。每个分类器都经过训练，以将一类受试者与所有其他类别的受试者区分开来。这些分类器的结果再使用真值矩阵进行组合，以确定最终结果。利用249名受试者的呼吸音特征，分类器试图将180名受试者分类为非OSA（呼吸暂停低通气指数小于5）或严重OSA（呼吸暂停低通气指数大于30）。分别有79%的OSA受试者和75%的非OSA受试者能够被分类。在那些被分类的受试者中，测试的敏感性和特异性分别为78%和86%。测试准确性与训练准确性的一致性表明了在数据集上使用多个专家分类器的稳健性和通用性。这项技术有可能在医生办公室用于快速且低成本地对OSA进行预筛查，以便医生能够更好地确定哪些患者需要进行夜间PSG检查。
