Heart Rate Dynamics in Patients with Obstructive Sleep Apnea: Heart Rate Variability and Entropy

Abstract

Background: Obstructive sleep apnea (OSA), a highly prevalent sleep disorder, is closely related to cardiovascular disease (CVD). Our previous work demonstrated that Shannon entropy of the degree distribution (EDD), obtained from the network domain of heart rate variability (HRV), might be a potential indicator for CVD. Method: To investigate the potential association between OSA and EDD, OSA patients and healthy controls (HCs) were identified from a sleep study database. Then EDD was calculated from electrocardiogram (ECG) signals during sleep, followed by cross-sectional comparisons between OSA patients and HCs, and longitudinal comparisons from baseline to follow-up visits. Furthermore, for OSA patients, the association between EDD and OSA severity, measured by apnea-hypopnea index (AHI), was also analyzed. Results: Compared with HCs, OSA patients had significantly increased EDD during sleep. A positive correlation between EDD and the severity of OSA was also observed. Although the value of EDD became larger with aging, it was not OSA-specified. Conclusion: Increased EDD derived from ECG signals during sleep might be a potential dynamic biomarker to identify OSA patients from HCs, which may be used in screening OSA with high risk before polysomnography is considered.

Heart rate variability and obstructive sleep apnea: Current perspectives and novel technologies

“…In the context of OSA, 15‐min ECG signals taken during the day reveal a lower mean value for SampEn in patients with OSA compared with healthy controls (Chang et al, 2013). Contrary to this, analysis of 5‐min segments during sleep reported in a recent study showed no significant differences to SampEn between patients with OSA and healthy controls during sleep (Zhang et al, 2019). However, this study was limited by a small sample of healthy controls ( n = 16), compared to patients with OSA ( n = 60).…”

Exploring the Hidden Complexity: Entropy Analysis in Pulse Oximetry of Female Athletes

“…Beyond diagnostics, entropy measures can be used to distinguish expert athletic performances [ 34 ] and between fallers and non-fallers [ 35 ], and identify various health aspects such as fall risks [ 35 ], effects of aging on gait [ 36 , 37 ], detect physical fatigue [ 38 ], respiratory dysfunctions like sleep apnea [ 39 ], stress responses [ 40 ], or report valuable information to running training methods [ 41 ] among others.…”

Cheyne-Stokes Respiration Perception via Machine Learning Algorithms

“…Chronic heart failure affects 0.9 percent of the general population, but the prevalence of the disease increases significantly with age, and they have a higher mortality rate; the five-year survival rate is only about 50% [6]. Among patients with more severe heart failure, Cheyne-Stokes respiration is often present, and the abnormal breathing pattern is an independent predictor of poor prognosis in patients with heart failure [7][8][9]. The emergence of Cheyne-Stokes respiration is a warning sign of worsening heart failure, and an independent risk factor for increased case fatality [10]; it is also closely related to the physical condition of the patient.…”