A Novel Heart Rate Variability Measurement Method for Detecting Obstructive Sleep Apnea Using Local Difference Visibility Graphs

Obstructive sleep apnea (OSA) is a common and chronic respiratory disorder. Autonomic dysfunction and heart rate (HR) change usually occur during apneic events. Damaged autonomic nerve system (ANS) modulation can lead to morphological differences in R-to-R intervals (RRI) series. Capable of capturing structural properties of RRI series and thus assessing the HR dynamics, the local difference visibility graphs (LDVGs) method was proposed as a novel OSA detection method in this study. The LDVG method focuses on the long continuous ascent range, which is likely to be induced by OSA attack, demonstrating the characteristics of bradycardia and tachycardia in OSA. Combined with the local strategy, it avoids heavy workload while maintaining major correlations. The degree mean of LDVG (LDVG_M) and degree uncertainty of LDVG (LDVG_SEn) were calculated as the mean and Shannon entropy of the degree sequence of LDVG, which can quantify the tightness and uncertainty of structural characteristics of the RR interval series, respectively. Sixty electrocardiograph (ECG) recordings from the Physionet database were used in this study. Both LDVG_M and LDVG_SEn achieved a good performance in differentiating the severity of diseases, while the classic index low-frequency power to high-frequency power ratio value (LF/HF) cannot. Then, binary classification was performed. In OSA detection, the accuracy, specificity, and sensitivity of both LDVG-related indices were all above 90%. LDVG_SEn achieved the highest accuracy (96.7%) while maintaining a good balance between sensitivity (97.5%) and specificity (95.0%). Therefore, this LDVG method provides an insight into the underlying pathological mechanism of OSA and is promising for clinical and home monitoring.