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

Abstract

This study focuses on the analysis of blood oxygen saturation (SaO(2)) 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 SaO(2) 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 ( P(R)), 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.

Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

“…Data from studies involving SpO2, airflow, snoring, respiratory effort, and HRV were included. In the case of the SpO2 signal, Acc and AROC range from 84.1% to 95% and 0.822 to 0.967, respectively [49][50][51][52]. A database composed of 187 recordings was used to model a multi-layer perceptron (MLP) classifier, which was obtained from three non-linear features [49].…”

“…Six spectral (3) and non-linear (3) features were extracted from the same database to obtain four more classifiers by means of linear and quadratic discriminant analysis, K-nearest neighbours (KNN), and LR [50]. The best diagnostic ability for SpO2 in terms of AROC (0.967) was achieved by a LR model obtained from four automatically-selected features extracted from the frequency and time domain of 147 recordings [51]. The best Acc (95.0%) was reported in the case of a support vector machine (SVM) classifier evaluated for a 5 e/h AHI threshold [52].…”

Automated Screening of Children With Obstructive Sleep Apnea Using Nocturnal Oximetry: An Alternative to Respiratory Polygraphy in Unattended Settings

“…Hence, sample entropy (SampEn) and central tendency measure (CTM) were used in this study to quantify irregularity and variability of SpO 2 recordings, respectively. [28][29][30][31] Features from the frequency domain provide information about the duration and the repetitive nature of apneic events. The power spectral density was computed using the wellknown Welch method, which is suitable for nonstationary signals.…”

“…The power spectral density was computed using the wellknown Welch method, which is suitable for nonstationary signals. 26,31 Next, a comprehensive analysis was conducted to properly derive a frequency region in the power spectrum related to the recurrence of desaturations in children. In order to obtain such a frequency band of interest, we searched for statistical significant differences in the power spectral density amplitude between OSA-positive and OSA-negative children for every single frequency component.…”

Positive airway pressure and electrical stimulation methods for obstructive sleep apnea treatment: a patent review (2005 – 2014)

“…In addition, automated signal processing techniques have been widely applied during the last years to improve the diagnostic ability of single-channel cardiorespiratory recordings. In this regard, single-lead ECG [27,28], single-channel AF [29,30,31] and blood oxygen saturation (SpO2) from nocturnal pulse oximetry [25,32,33] have been predominantly studied. In order to standardize portable monitoring assessment and help 7 physicians to uniformly classify and properly evaluate sleep testing devices, different studies have been carried out during the last years [24,26,34].…”