Abstract
Our contribution focuses on the characterization of sleep apnea from a cardiac rate point of view, using Recurrence Quantification Analysis (RQA), based on a Heart Rate Variability (HRV) feature selection process. Three parameters are crucial in RQA: those related to the embedding process (dimension and delay) and the threshold distance. There are no overall accepted parameters for the study of HRV using RQA in sleep apnea. We focus on finding an overall acceptable combination, sweeping a range of values for each of them simultaneously. Together with the commonly used RQA measures, we include features related to recurrence times, and features originating in the complex network theory. To the best of our knowledge, no author has used them all for sleep apnea previously. The best performing feature subset is entered into a Linear Discriminant classifier. The best results in the “Apnea-ECG Physionet database” and the “HuGCDN2014 database” are, according to the area under the receiver operating characteristic curve, 0.93 (Accuracy: 86.33%) and 0.86 (Accuracy: 84.18%), respectively. Our system outperforms, using a relatively small set of features, previously existing studies in the context of sleep apnea. We conclude that working with dimensions around 7–8 and delays about 4–5, and using for the threshold distance the Fixed Amount of Nearest Neighbours (FAN) method with 5% of neighbours, yield the best results. Therefore, we would recommend these reference values for future work when applying RQA to the analysis of HRV in sleep apnea. We also conclude that, together with the commonly used vertical and diagonal RQA measures, there are newly used features that contribute valuable information for apnea minutes discrimination. Therefore, they are especially interesting for characterization purposes. Using two different databases supports that the conclusions reached are potentially generalizable, and are not limited by database variability.
Highlights
Obstructive Sleep Apnea (OSA) is a widespread sleep respiratory disorder, characterized by repetitive breathing pauses due to upper airway collapse during sleep
The general goal of all data analysis presented in this article is to detect the parameter combination that reaches the best discriminant capacity between apnea and nonapnea minutes when using Recurrence Quantification Analysis (RQA) applied to sleep apnea
Following Shinckel et al [78], we use the area under the curve (AUC) of the receiver operating curve (ROC) as the main performance measure of the system, as it can be considered a summary of the ROC
Summary
Obstructive Sleep Apnea (OSA) is a widespread sleep respiratory disorder, characterized by repetitive breathing pauses due to upper airway collapse during sleep. It can be considered a public health problem, because of its high prevalence, 4% in men and 2% in women [1], and because of its major health implications [2,3,4,5]. The criterion used to decide whether a patient suffers from OSA is the mean number of apneas per hour of sleep: Apnea-Hypopnea Index (AHI) [19]. Subjects with AHIs greater than 5 are OSA diagnosed and ranked according to the following: AHI ranging [5,15]: mild sleep apnea; AHI ranging [15, 30]: moderate sleep apnea; and AHI greater than 30: severe sleep apnea
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