Kaimakamis Evangelos, Tsara Venetia, Bratsas Charalambos, Sichletidis Lazaros, Karvounis Charalambos, Maglaveras Nikolaos
Lab of Medical Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Sleep Unit, Pulmonary Department, General Hospital "G. Papanikolaou," Thessaloniki, Greece.
PLoS One. 2016 Mar 3;11(3):e0150163. doi: 10.1371/journal.pone.0150163. eCollection 2016.
Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder.
Patients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining application with contemporary classification algorithms for the creation of predictive models for AHI.
A linear regression model presented a correlation coefficient of 0.77 in predicting AHI. With a cutoff value of AHI = 8, the sensitivity and specificity were 93% and 71.4% in discrimination between patients and normal subjects. The decision tree for the discrimination between patients and normal had sensitivity and specificity of 91% and 60%, respectively. Certain obtained nonlinear values correlated significantly with commonly accepted physiological parameters of people suffering from OSA.
We developed a predictive model for the presence/severity of OSA using a simple linear equation and additional decision trees with nonlinear features extracted from 3 respiratory recordings. The accuracy of the methodology is high and the findings provide insight to the underlying pathophysiology of the syndrome.
Reliable predictions of OSA are possible using linear and nonlinear indices from only 3 respiratory signals during sleep. The proposed models could lead to a better study of the pathophysiology of OSA and facilitate initial evaluation/follow up of suspected patients OSA utilizing a practical low cost methodology.
ClinicalTrials.gov NCT01161381.
阻塞性睡眠呼吸暂停（OSA）是一种常见的睡眠障碍，诊断需要耗费时间和金钱的多导睡眠监测。人们正在寻找初始评估的替代方法。我们的目的是基于睡眠期间呼吸生物信号的非线性分析来预测可能患有OSA患者的呼吸暂停低通气指数（AHI），该方法与该疾病的病理生理学相关。
转诊至睡眠科的135例患者接受了全面的多导睡眠监测。从两个生物信号（鼻导管气流、胸部运动）中提取的三个非线性指标（最大Lyapunov指数、去趋势波动分析和近似熵）以及从血氧饱和度得出的一个线性指标为数据挖掘应用提供输入，该应用采用当代分类算法来创建AHI预测模型。
线性回归模型在预测AHI方面的相关系数为0.77。AHI = 8作为临界值时，在区分患者与正常受试者方面，敏感性和特异性分别为93%和71.4%。区分患者与正常受试者的决策树的敏感性和特异性分别为91%和60%。某些获得的非线性值与OSA患者普遍接受的生理参数显著相关。
我们使用一个简单的线性方程以及从3个呼吸记录中提取非线性特征的附加决策树，开发了一个用于OSA存在/严重程度的预测模型。该方法的准确性很高，研究结果为该综合征的潜在病理生理学提供了见解。
仅使用睡眠期间3个呼吸信号的线性和非线性指标就可以对OSA进行可靠预测。所提出的模型可以更好地研究OSA的病理生理学，并利用实用的低成本方法促进对疑似OSA患者的初始评估/随访。
ClinicalTrials.gov NCT01161381。
