Combining Heart Rate Variability and Oximetry to Improve Apneic Event Screening in Non-Desaturating Patients.

In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO2, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO2-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study.

Improving the understanding of sleep apnea characterization using Recurrence Quantification Analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold.

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.

Heart rate variability feature selection in the presence of sleep apnea: An expert system for the characterization and detection of the disorder.

We introduce a sleep apnea characterization and classification approach based on a Heart Rate Variability (HRV) feature selection process, thus focusing on the characterization of the underlying process from a cardiac rate point of view. Therefore, we introduce linear and nonlinear variables, namely Cepstrum Coefficients (CC), Filterbanks (Fbank) and Detrended Fluctuation Analysis (DFA). Logistic Regression, Linear Discriminant Analysis and Quadratic Discriminant Analysis were used for classification purposes. The experiments were carried out using two databases. We achieved a per-segment accuracy of 84.76% (sensitivity = 81.45%, specificity = 86.82%, AUC = 0.92) in the Apnea-ECG Physionet database, whereas in the HuGCDN2014 database, provided by the Dr. Negrín University Hospital (Las Palmas de Gran Canaria, Spain), the best results were: accuracy = 81.96%, sensitivity = 70.95%, specificity = 85.47%, AUC = 0.87. The former results were comparable or better than those obtained by other methods for the same database in the recent literature. We have concluded that the selected features that best characterize the underlying process are common to both databases. This supports the fact that the conclusions reached are potentially generalizable. The best results were obtained when the three kinds of features were jointly used. Another notable fact is the small number of features needed to describe the phenomenon. Results suggest that the two first Fbanks, the first CC and the first DFA coefficient are the variables that best describe the RR pattern in OSA and, therefore, are especially relevant to extract discriminative information for apnea screening purposes.