Feature selection from nocturnal oximetry using genetic algorithms to assist in obstructive sleep apnoea diagnosis
Feature selection from nocturnal oximetry using genetic algorithms to assist in obstructive sleep apnoea diagnosis
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DOI：
10.1016/j.medengphy.2011.11.009
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发表时间：
2012-10-01
影响因子：
2.2
通讯作者：
del Campo, Felix
中科院分区：
文献类型：
--
作者：
Alvarez, Daniel;Hornero, Roberto;del Campo, Felix
夜间脉搏血氧饱和度（NPO）已被证明是一个强大的工具，以帮助阻塞性睡眠呼吸暂停（OSA）的检测。然而，需要额外的分析来单独使用NPO作为夜间多导睡眠图（NPSG）的替代方案，NPSG是明确诊断的金标准。在本研究中，我们详尽地分析了血氧饱和度（SpO（2））记录（80 OSA阴性和160 OSA阳性）的数据库，以进一步了解NPO的有用性。将群体集随机分为训练集和测试集。进行特征提取阶段：计算16个特征（时间和频率统计以及频谱和非线性特征）。一种遗传算法（GA）的方法被应用在特征选择阶段。我们的方法实现了87.5%的准确性（90.6%的灵敏度和81.3%的特异性），在测试集使用逻辑回归（LR）分类器与数量减少的互补功能（3个时域统计，1频域统计，1个传统的频谱特征和1个非线性特征）自动选择的装置气体。我们的研究结果提高了医生常用的常规血氧指数的诊断性能。我们的结论是，气体可以是一个有效的和强大的工具，以寻找基本的血氧功能，可以提高NPO在OSA诊断的背景下。(c)2011年IPEM。由爱思唯尔有限公司出版。保留所有权利。
Nocturnal pulse oximetry (NPO) has demonstrated to be a powerful tool to help in obstructive sleep apnoea (OSA) detection. However, additional analysis is needed to use NPO alone as an alternative to nocturnal polysomnography (NPSG), which is the gold standard for a definitive diagnosis. In the present study, we exhaustively analysed a database of blood oxygen saturation (SpO(2)) recordings (80 OSA-negative and 160 OSA-positive) to obtain further knowledge on the usefulness of NPO. Population set was randomly divided into training and test sets. A feature extraction stage was carried out: 16 features (time and frequency statistics and spectral and nonlinear features) were computed. A genetic algorithm (GA) approach was applied in the feature selection stage. Our methodology achieved 87.5% accuracy (90.6% sensitivity and 81.3% specificity) in the test set using a logistic regression (LR) classifier with a reduced number of complementary features (3 time domain statistics, 1 frequency domain statistic, 1 conventional spectral feature and 1 nonlinear feature) automatically selected by means of GAs. Our results improved diagnostic performance achieved with conventional oximetric indexes commonly used by physicians. We concluded that GAs could be an effective and robust tool to search for essential oximetric features that could enhance NPO in the context of OSA diagnosis. (c) 2011 IPEM. Published by Elsevier Ltd. All rights reserved.