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Long-term effect of continuous positive airway pressure in hypertensive patients with sleep apnea.

RATIONALE Continuous positive airway pressure (CPAP) is the current treatment for patients with symptomatic obstructive sleep apnea (OSA). Its use for all subjects with sleep-disordered breathing, regardless of daytime symptoms, is unclear. OBJECTIVES This multicenter controlled trial assesses the effects of 1 year of CPAP treatment on blood pressure (BP) in nonsymptomatic, hypertensive patients with OSA. METHODS We evaluated 359 patients with OSA. Inclusion criteria consisted of an apnea-hypopnea index (AHI) greater than 19 hour(-1), an Epworth Sleepiness Scale score less than 11, and one of the following: under antihypertensive treatment or systolic blood pressure greater than 140 or diastolic blood pressure greater than 90 mm Hg. Patients were randomized to CPAP (n = 178) or to conservative treatment (n = 181). BP was evaluated at baseline and at 3, 6, and 12 months of follow-up. MEASUREMENTS AND MAIN RESULTS Mean (SD) values were as follows: age, 56 +/- 10 years; body mass index (BMI), 32 +/- 5 kg x m(-2); AHI, 45 +/- 20 hour(-1); and Epworth Sleepiness Scale score, 7 +/- 3. After adjusting for follow-up time, baseline blood pressure values, AHI, time with arterial oxygen saturation less than 90%, and BMI, together with the change in BMI at follow-up, CPAP treatment decreased systolic blood pressure by 1.89 mm Hg (95% confidence interval: -3.90, 0.11 mm Hg; P = 0.0654), and diastolic blood pressure by 2.19 mm Hg (95% confidence interval: -3.46, -0.93 mm Hg; P = 0.0008). The most significant reduction in BP was in patients who used CPAP for more than 5.6 hours per night. CPAP compliance was related to AHI and the decrease in Epworth Sleepiness Scale score. CONCLUSIONS In nonsleepy hypertensive patients with OSA, CPAP treatment for 1 year is associated with a small decrease in BP. This effect is evident only in patients who use CPAP for more than 5.6 hours per night. Clinical trial registered with www.clinicaltrials.gov (NCT00127348).

Multivariate Analysis of Blood Oxygen Saturation Recordings in Obstructive Sleep Apnea Diagnosis

This study focuses on the analysis of blood oxygen saturation (SaO2) from nocturnal pulse oximetry (NPO) to help in the diagnosis of the obstructive sleep apnea (OSA) syndrome. A population of 148 patients suspected of suffering from OSA syndrome was studied. A wide set of 16 features was used to characterize changes in the SaO2 profile during the night. Our feature set included common statistics in the time and frequency domains, conventional spectral characteristics from the power spectral density (PSD) function, and nonlinear features. We performed feature selection by means of a step-forward logistic regression (LR) approach with leave-one-out cross-validation. Second- and fourth-order statistical moments in the time domain (M2t and M4t), the relative power in the 0.014-0.033 Hz frequency band (PR), and the Lempel-Ziv complexity (LZC) were automatically selected. 92.0% sensitivity, 85.4% specificity, and 89.7% accuracy were obtained. The optimum feature set significantly improved the diagnostic ability of each feature individually. Furthermore, our results outperformed classic oximetric indexes commonly used by physicians. We conclude that simultaneous analysis in the time and frequency domains by means of statistical moments, spectral and nonlinear features could provide complementary information from NPO to improve OSA diagnosis.

Improving diagnostic ability of blood oxygen saturation from overnight pulse oximetry in obstructive sleep apnea detection by means of central tendency measure

OBJECTIVES Nocturnal pulse oximetry is a widely used alternative to polysomnography (PSG) in screening for obstructive sleep apnea (OSA) syndrome. Several oximetric indexes have been derived from nocturnal blood oxygen saturation (SaO2). However, they suffer from several limitations. The present study is focused on the usefulness of nonlinear methods in deriving new measures from oximetry signals to improve the diagnostic accuracy of classical oximetric indexes. Specifically, we assessed the validity of central tendency measure (CTM) as a screening test for OSA in patients clinically suspected of suffering from this disease. MATERIALS AND METHODS We studied 187 subjects suspected of suffering from OSA referred to the sleep unit. A nocturnal pulse oximetry study was applied simultaneously to a conventional PSG. Three different index groups were compared. The first one was composed by classical indexes provided by our oximeter: oxygen desaturation indexes (ODIs) and cumulative time spent below a saturation of 90% (CT90). The second one was formed by indexes derived from a nonlinear method previously studied by our group: approximate entropy (ApEn). The last one was composed by indexes derived from a CTM analysis. RESULTS For a radius in the scatter plot equal to 1, CTM values corresponding to OSA positive patients (0.30+/-0.20, mean+/-S.D.) were significantly lower (p<<0.001) than those values from OSA negative subjects (0.71+/-0.18, mean+/-S.D.). CTM was significantly correlated with classical indexes and indexes from ApEn analysis. CTM provided the highest correlation with the apnea-hipopnea index AHI (r=-0.74, p<0.0001). Moreover, it reached the best results from the receiver operating characteristics (ROC) curve analysis, with 90.1% sensitivity, 82.9% specificity, 88.5% positive predictive value, 85.1% negative predictive value, 87.2% accuracy and an area under the ROC curve of 0.924. Finally, the AHI derived from the quadratic regression curve for the CTM showed better agreement with the AHI from PSG than classical and ApEn derived indexes. CONCLUSION The results suggest that CTM could improve the diagnostic ability of SaO2 signals recorded from portable monitoring. CTM could be a useful tool for physicians in the diagnosis of OSA syndrome.

Assessment of four statistical pattern recognition techniques to assist in obstructive sleep apnoea diagnosis from nocturnal oximetry

The aim of this study is to assess the utility of traditional statistical pattern recognition techniques to help in obstructive sleep apnoea (OSA) diagnosis. Classifiers based on quadratic (QDA) and linear (LDA) discriminant analysis, K-nearest neighbours (KNN) and logistic regression (LR) were evaluated. Spectral and nonlinear input features from oxygen saturation (SaO(2)) signals were applied. A total of 187 recordings from patients suspected of suffering from OSA were available. This initial dataset was divided into training set (74 subjects) and test set (113 subjects). Twelve classification algorithms were developed by applying QDA, LDA, KNN and LR with spectral features, nonlinear features and combination of both groups. The performance of each algorithm was measured on the test set by means of classification accuracy and receiver operating characteristic (ROC) analysis. QDA, LDA and LR showed better classification capability than KNN. The classifier based on LDA with spectral features provided the best diagnostic ability with an accuracy of 87.61% (91.05% sensitivity and 82.61% specificity) and an area under the ROC curve (AROC) of 0.925. The proposed statistical pattern recognition techniques could be applied as an OSA screening tool.

Oxygen saturation regularity analysis in the diagnosis of obstructive sleep apnea

OBJECTIVE The present study assessed the validity of approximate entropy (ApEn) analysis of arterial oxygen saturation (SaO(2)) data obtained from pulse oximetric recordings as a diagnostic test for obstructive sleep apnea (OSA) in patients clinically suspected of suffering this disease. METHODOLOGY A sample of 187 referred outpatients, clinically suspected of having OSA, was studied using nocturnal pulse oximetric recording performed simultaneously with complete polysomnography. ApEn analysis was applied to SaO(2) data. RESULTS Patients with OSA presented significantly higher approximate entropy levels than those without OSA (1.08+/-0.30 versus 0.47+/-0.26). Apnea-hypopnea index was correlated significantly with ApEn (r=0.607; p<0.001). Using receiver operating characteristic curve analysis, we obtained a diagnostic sensitivity of 88.3% and specificity of 82.9%, positive predictive value of 88.3% and a negative predictive value of 82.9%, at a threshold of 0.679. As a diagnostic test, this method presents high sensitivity and specificity compared to traditional methods in the diagnosis of OSA. CONCLUSION We conclude that ApEn analysis of SaO(2) data obtained from pulse oximetric recordings could be useful as a diagnostic technique for OSA subjects.

Automated detection of obstructive sleep apnoea syndrome from oxygen saturation recordings using linear discriminant analysis.

Nocturnal polysomnography (PSG) is the gold-standard to diagnose obstructive sleep apnoea syndrome (OSAS). However, it is complex, expensive, and time-consuming. We present an automatic OSAS detection algorithm based on classification of nocturnal oxygen saturation (SaO2) recordings. The algorithm makes use of spectral and nonlinear analysis for feature extraction, principal component analysis (PCA) for preprocessing and linear discriminant analysis (LDA) for classification. We conducted a study to characterize and prospectively validate our OSAS detection algorithm. The population under study was composed of subjects suspected of suffering from OSAS. A total of 214 SaO2 signals were available. These signals were randomly divided into a training set (85 signals) and a test set (129 signals) to prospectively validate the proposed method. The OSAS detection algorithm achieved a diagnostic accuracy of 93.02% (97.00% sensitivity and 79.31% specificity) on the test set. It outperformed other alternative implementations that either use spectral and nonlinear features separately or are based on logistic regression (LR). The proposed method could be a useful tool to assist in early OSAS diagnosis, contributing to overcome the difficulties of conventional PSG.

Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry

The aim of this study is to assess the ability of radial basis function (RBF) classifiers as an assistant tool for the diagnosis of the obstructive sleep apnoea syndrome (OSAS). A total of 187 subjects suspected of suffering from OSAS were available for our research. The initial population was divided into training, validation and test sets for deriving and testing our neural classifiers. We used nonlinear features from nocturnal oxygen saturation (SaO2) to perform patients’ classification. We evaluated three different RBF construction techniques based on the following algorithms: k-means (KM), fuzzy c-means (FCM) and orthogonal least squares (OLS). A diagnostic accuracy of 86.1, 84.7 and 85.5% was provided by the networks developed with KM, FCM and OLS, respectively. The three proposed networks achieved an area under the receiver operating characteristic (ROC) curve over 0.90. Our results showed that a useful non-invasive method could be applied to diagnose OSAS from nonlinear features of SaO2 with RBF classifiers.

Assessment of feature selection and classification approaches to enhance information from overnight oximetry in the context of apnea diagnosis.

This study is aimed at assessing the usefulness of different feature selection and classification methodologies in the context of sleep apnea hypopnea syndrome (SAHS) detection. Feature extraction, selection and classification stages were applied to analyze blood oxygen saturation (SaO2) recordings in order to simplify polysomnography (PSG), the gold standard diagnostic methodology for SAHS. Statistical, spectral and nonlinear measures were computed to compose the initial feature set. Principal component analysis (PCA), forward stepwise feature selection (FSFS) and genetic algorithms (GAs) were applied to select feature subsets. Fishers linear discriminant (FLD), logistic regression (LR) and support vector machines (SVMs) were applied in the classification stage. Optimum classification algorithms from each combination of these feature selection and classification approaches were prospectively validated on datasets from two independent sleep units. FSFS + LR achieved the highest diagnostic performance using a small feature subset (4 features), reaching 83.2% accuracy in the validation set and 88.7% accuracy in the test set. Similarly, GAs + SVM also achieved high generalization capability using a small number of input features (7 features), with 84.2% accuracy on the validation set and 84.5% accuracy in the test set. Our results suggest that reduced subsets of complementary features (25% to 50% of total features) and classifiers with high generalization ability could provide high-performance screening tools in the context of SAHS.

Spectral analysis of electroencephalogram and oximetric signals in obstructive sleep apnea diagnosis

This study assessed the hypothesis that blood oxygen saturation (SaO2) and electroencephalogram (EEG) recordings could provide complementary information in the diagnosis of the obstructive sleep apnea (OSA) syndrome. We studied 148 patients suspected of suffering from OSA. Classical spectral parameters based on the relative power in specified frequency bands (Af-band) or peak amplitudes (PA) were used to characterize the frequency content of SaO2 and EEG recordings. Additionally, the median frequency (MF) and the spectral entropy (SE) were applied to obtain further spectral information. We applied a forward stepwise logistic regression (LR) procedure with crossvalidation leave-one-out to obtain the optimum spectral feature set. Two features from the oximetric spectral analysis (PA and MFsat) and three features from the EEG spectral analysis (Adelta, Aalpha and SEeeg) were automatically selected. 91.0% sensitivity, 83.3% specificity and 88.5% accuracy were obtained. These results suggest that MF and SE could provide additional information to classical frequency characteristics commonly used in OSA diagnosis. Additionally, nocturnal SaO2 and EEG recordings during the whole night could provide complementary information to help in the detection of OSA syndrome.

Utility of multilayer perceptron neural network classifiers in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry

The aim of this study is to assess the ability of multilayer perceptron (MLP) neural networks as an assistant tool in the diagnosis of the obstructive sleep apnoea syndrome (OSAS). Non-linear features from nocturnal oxygen saturation (SaO(2)) recordings were used to discriminate between OSAS positive and negative patients. A total of 187 subjects suspected of suffering from OSAS (111 with a positive diagnosis of OSAS and 76 with a negative diagnosis of OSAS) took part in the study. The initial population was divided into training, validation and test sets for deriving and testing our neural network classifier. Three methods were applied to extract non-linear features from SaO(2) signals: approximate entropy (ApEn), central tendency measure (CTM) and Lempel-Ziv complexity (LZC). The selected MLP-based classifier provided a diagnostic accuracy of 85.5% (89.8% sensitivity and 79.4% specificity). Our neural network algorithm could represent a useful technique for OSAS detection. It could contribute to reduce the demand for polysomnographic studies in OSAS screening.