Entropy-based measures of EEG arousals as biomarkers for sleep dynamics: applications to hypertension.

TRADITIONAL INDICES DERIVED FROM POLYSOMNOGRAPHY (PSG) DATA THAT ARE AVAILABLE TO CHARACTERIZE CLINICAL DISEASES SUCH AS SLEEP disordered breathing (SDB), include AHI (apnea-hypopnea index), ArI (arousal index), and SE (sleep efficiency).1,2 Although sleep researchers have used these indices to establish associations between SDB and other diseases and to quantify the severity of SDB,3,4 they do not capture or quantify the dynamic patterns of the sleep process. To date, very little attention has been given to the temporal patterns of sleep and their relationship to health, the natural aging process, and disease. Current PSG assessments use Rechtschaffen and Kales approaches5 to assign the discrete sleep stages to 30-sec epochs, and use American Academy of Sleep Medicine (AASM) definitions to identify discrete events such as apneas, hypopneas, and arousals. Clinical and epidemiological studies often base their primary inferences on these well-established standards, which historically have formed the basis for research inferences and clinical practice, and also constitute the basis for third party reimbursement. However, recent innovations in digital acquisition and processing of PSG signals have identified opportunities for quantifying sleep architecture and SDB that may provide more sensitive measurement of sleep physiology than do the traditional scoring approaches. It is well known that the temporal patterns of physiological signals can provide unique insight into the differences between the normal and disease states in both humans and animals, and often these patterns can provide biomarkers that are predictive of a trend toward an abnormal state, as well as a better determination of changes in the state of activation of the autonomic nervous system.6 For example, subtle changes in temporal heart rate and electroencephalographic rhythms have been shown to be indicative of a change in physiological condition, such as a trend toward chronic heart failure or the onset of an epileptic seizure.7–11 In both of these instances, the heart rate and EEG signals respectively undergo gradual and detectable changes where the natural “complexity” of the underlying physiological signals gives way to more regularity as the disease state progresses. Entropy measures12,13 are gaining popularity in biological data analysis because of their ability to discriminate regular dynamics from irregular dynamics. Entropy measures can quantify the complexity of measured observation signals without information about the underlying dynamics of the system. One way to measure the complexity of a time series is to quantify the regularity or periodicity, of the temporal patterns using conditional entropy.14 In our application, conditional entropy is used to quantify the periodicity or regularity of a short data sequence (the arousal events time series) through an analysis of the repetition of temporal patterns in the data. In this study, we hypothesize that a change in the regularity of a biological signal, such as arousal events, can be used as a biomarker of underlying disease processes. We focus on EEG arousals because the frequency of arousals has been identified as a marker of sympathetic nervous system activation that may be associated with propensity for hypertension,15 as well as a marker for sleep discontinuity predisposing to daytime sleepiness. The data relating arousal frequency to health outcomes have been inconsistent, however. In part this may relate to difficulties in reliably scoring arousals16 or because the arousal index (ArI) measures the total number of arousals per hour of sleep and does not capture the dynamic behavior of the arousal events and their distribution in both time and intensity (duration). Given other systems in which additional information was derived by quantifying temporal patterns and regularity of events,6 we propose to treat the arousal events as a dynamic process (time series data) by characterizing the temporal patterns of this time series to determine if such an approach provides alternative information compared to the ArI. Accordingly, the strength of association of hypertension, a condition associated with increased arousal frequency, with traditional PSG measures is compared to the association with temporally derived PSG measures.