We propose a generation of Polysomnography (PSG)-derived measures that can quantify temporal patterns of sleep, and investigate the role of these measures as predictors of hypertension. We also investigate the influence of age on these measures as compared to traditional indices. We perform cross-sectional analyses of the association between hypertension status with traditional PSG and novel measures using adjusted and unadjusted logistic regression models. Our findings suggest that when adjusted for common confounders such as age, gender, race and Body Mass Index (BMI) the new features that quantify the variability of the sleep process are more strongly associated with hypertension as compared to traditional PSG indices, and are not as strongly influenced by age as are the traditional indices. The result implies that the regularity of sleep dynamics may be an important feature associated with hypertension. These measures may provide a powerful tool for discriminating individuals at risk for comorbidities, such as hypertension, associated with sleep disturbances.