Combining Heart Rate Variability and SpO2 features achieved a success rate of 95.74%, 100% sensitivity, and 89.47% specificity for the global diagnosis of OSA in non-desaturating patients.
Observational
Does combining HRV and SpO2 features improve the detection of apneic events in non-desaturating OSA patients?
Combining HRV and SpO2 features provides highly accurate detection of apneic events, particularly in patients who do not exhibit oxygen desaturation.
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.
González et al. (Tue,) conducted a observational in Obstructive Sleep Apnea (OSA). Combining Heart Rate Variability (HRV) and SpO2 features using LDA classifier vs. Individual features was evaluated on Global diagnosis of OSA patients (HuGCDN2014-OXI). Combining Heart Rate Variability and SpO2 features achieved a success rate of 95.74%, 100% sensitivity, and 89.47% specificity for the global diagnosis of OSA in non-desaturating patients.