A Classifying Model of Obstructive Sleep Apnea Based on Heart Rate Variability in a Large Korean Population

Abstract

Background The majority of patients with obstructive sleep apnea do not receive timely diagnosis and treatment because of the complexity of a diagnostic test. We aimed to predict obstructive sleep apnea based on heart rate variability, body mass index, and demographic characteristics in a large Korean population. Methods Models of binary classification for predicting obstructive sleep apnea severity were constructed using 14 features including 11 heart rate variability variables, age, sex, and body mass index. Binary classification was conducted separately using apnea-hypopnea index thresholds of 5, 15, and 30. Sixty percent of the participants were randomly allocated to training and validation sets while the other forty percent were designated as the test set. Classifying models were developed and validated with 10-fold cross-validation using logistic regression, random forest, support vector machine, and multilayer perceptron algorithms. Results A total of 792 (651 men and 141 women) subjects were included. The mean age, body mass index, and apnea-hypopnea index score were 55.1 years, 25.9 kg/m 2 , and 22.9, respectively. The sensitivity of the best performing algorithm was 73.6%, 70.7%, and 78.4% when the apnea-hypopnea index threshold criterion was 5, 10, and 15, respectively. The prediction performances of the best classifiers at apnea-hypopnea indices of 5, 15, and 30 were as follows: accuracy, 72.2%, 70.0%, and 70.3%; specificity, 64.6%, 69.2%, and 67.9%; area under the receiver operating characteristic curve, 77.2%, 73.5%, and 80.1%, respectively. Overall, the logistic regression model using the apnea-hypopnea index criterion of 30 showed the best classifying performance among all models. Conclusion Obstructive sleep apnea was fairly predicted by using heart rate variability, body mass index, and demographic characteristics in a large Korean population. Prescreening and continuous treatment monitoring of obstructive sleep apnea may be possible simply by measuring heart rate variability.

Hypertension: Causes and Consequences of Circadian Rhythms in Blood Pressure

“…A decrease in CBF under these conditions may require simultaneous cerebrovascular disease (eg, intracranial atherosclerosis, endothelial dysfunction, and loss of autoregulatory capacity). 158,159,161 Some have noted it is unlikely that reduced cardiac output alone can explain decreases in CBF during heart failure. 160…”

“…153 Associations between heart disease and white matter abnormalities have also been described. 154 For each of these forms of cardiac disease, it has been speculated that reductions in cardiac output, 15,155–160 or reductions in cardiac output and BP, 146,157,161 result in decreased CBF (Figure 2). The latter effect is often assumed to occur and contribute to a progressive decline in cognitive function.…”