title: Entropy-based measures of EEG arousals as biomarkers for sleep dynamics: applications to hypertension.
abstract: OBJECTIVE
  We propose a generation of PSG-derived measures that using entropy 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 entropy-based measures as compared to traditional indices.
  
  
  METHODS
  Cross-sectional analyses of the association between hypertension status with traditional PSG and novel measures using adjusted and unadjusted logistic regression models. The novel measures were developed to quantify variability of the arousal event process.
  
  
  METHODS
  Analyses were based on a subsample of subjects from the Cleveland Family Study with clearly disparate hypertension status.
  
  
  RESULTS
  Among traditional PSG indices, the apnea hypopnea index (AHI) has the highest Odds Ratio (unadjusted and adjusted for age, gender, race, BMI: OR = 2.36 (95% CI: 1.48, 3.75, P = 0.0003) and 1.18, (95% CI: 0.76, 1.84, P = 0.46), respectively). The best predictor among the entropy-based measures is derived from analysis of the temporal patterns of arousal duration with unadjusted and adjusted ORs of 1.36 (95% CI: 1.08, 1.71, P = 0.0085) and 2.08 (95% CI: 1.19, 3.64, P = 0.01), respectively.
  
  
  CONCLUSIONS
  Our findings suggest that when adjusted for common confounders such as age, gender, race, and BMI, the entropy-based features that quantify the variability of the arousal event process are more strongly associated with hypertension as compared to traditional PSG indices; they are not as strongly influenced by age as are the traditional indices. The result implies that the regularity of arousals 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.
authors.affiliation: Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA. rezaj@case.edu
authors.forename: Reza
authors.initials: R
authors.lastname: Jamasebi
authors.forename: Susan
authors.initials: S
authors.lastname: Redline
authors.forename: Sanjay R
authors.initials: SR
authors.lastname: Patel
authors.forename: Kenneth A
authors.initials: KA
authors.lastname: Loparo
mesh_terms: D000328:Adult; D001143:Arousal / Q000502:physiology*; D015415:Biomarkers; D001794:Blood Pressure / Q000235:genetics / Q000502:physiology; D015992:Body Mass Index; D002540:Cerebral Cortex / Q000503:physiopathology; D004569:Electroencephalography; D019277:Entropy; D005260:Female; D005583:Fourier Analysis; D020022:Genetic Predisposition to Disease / Q000235:genetics; D006801:Humans; D006973:Hypertension / Q000235:genetics / Q000503:physiopathology*; D008297:Male; D008875:Middle Aged; D010641:Phenotype; D017286:Polysomnography; D011237:Predictive Value of Tests; D012307:Risk Factors; D012815:Signal Processing, Computer-Assisted; D012891:Sleep Apnea Syndromes / Q000235:genetics / Q000503:physiopathology; D013223:Statistics as Topic
chemical_list: D015415:Biomarkers