Correntropy-based pulse rate variability analysis in children with sleep disordered breathing

Abstract

Pulse rate variability (PRV), an alternative measure of heart rate variability (HRV), is altered during obstructive sleep apnea. Correntropy spectral density (CSD) is a novel spectral analysis that includes nonlinear information. We recruited 160 children and recorded SpO2 and photoplethysmography (PPG), alongside standard polysomnography. PPG signals were divided into 1-min epochs and apnea/hypoapnea (A/H) epochs labeled. CSD was applied to the pulse-to-pulse interval time series (PPIs) and five features extracted: the total spectral power (TP: 0.01-0.6 Hz), the power in the very low frequency band (VLF: 0.01-0.04 Hz), the normalized power in the low and high frequency bands (LFn: 0.04-0.15 Hz, HFn: 0.15-0.6 Hz), and the LF/HF ratio. Nonlinearity was assessed with the surrogate data technique. Multivariate logistic regression models were developed for CSD and power spectral density (PSD) analysis to detect epochs with A/H events. The CSD-based features and model identified epochs with and without A/H events more accurately relative to PSD-based analysis (area under the curve (AUC) 0.72 vs. 0.67) due to the nonlinearity of the data. In conclusion, CSD-based PRV analysis provided enhanced performance in detecting A/H epochs, however, a combination with overnight SpO2 analysis is suggested for optimal results.