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Background Beat‐to‐beat blood pressure variability (BPV) is associated with an increased risk of stroke but can be driven by both healthy physiological processes and failure of compensatory mechanisms. Blood pressure (BP) complexity measures structured, organized variations in BP, as opposed to random fluctuations, and its reduction may therefore identify pathological beat‐to‐beat BPV. Methods and Results In the prospective, population‐based OXVASC (Oxford Vascular Study) Phenotyped Cohort with transient ischemic attack or minor stroke, patients underwent at least 5 minutes of noninvasive beat‐to‐beat monitoring of BP (Finometer) and ECG to derive the following: BPV (coefficient of variation) and complexity (modified multiscale entropy) of systolic BP and diastolic BP, heart rate variability (SD of R‐R intervals), and baroreflex sensitivity (BRS; Welch's method), in low‐ (0.04–0.15 Hz) and high‐frequency (0.15–0.4 Hz) bands. Associations between BPV or BP complexity with autonomic indexes and arterial stiffness were determined (linear regression), unadjusted, and adjusted for age, sex, and cardiovascular risk factors. In 908 consecutive, consenting patients, BP complexity was inversely correlated with BPV coefficient of variation ( P P r = 0.20; BRS low frequency: 0.19; BRS high frequency: 0.26) and arterial stiffness (pulse wave velocity: −0.21; all P P Conclusions Loss of BP complexity discriminates BPV because of pathological failure of compensatory mechanisms and may represent a less confounded and potentially modifiable risk factor for stroke.

Cerebral autoregulation (CA) is an important mechanism for maintaining constant cerebral blood flow during changes in blood pressure. Although previous studies have shown that CA may be impaired in patients with intracerebral hemorrhage (ICH), the variability of cerebral blood flow (CBF) in response to changes in CA has not been investigated. In the present study, we recruited twelve patients presenting with acute subcortical ICH and seven non-stroke controls. The status of CA was determined by assessing Pearson’s moving correlation coefficient between arterial blood pressure and cerebral blood flow velocity (CBFV) of the middle cerebral artery (MCA). The variability of CBFV was calculated using nonlinear sample entropy analyses. The results indicated that ICH patients significantly showed poorer CA than controls, particularly localized to the MCA ipsilateral to the ICH (controls 0.17 ± 0.13 versus ICH side 0.41 ± 0.27 and non-ICH side 0.34 ± 0.26, p = 0.044 and 0.120, respectively). Moreover, the variability of CBFV in the hemisphere ipsilateral to the ICH was significantly increased in ICH patients when compared to controls (1.145 ± 0.26 versus 1.66 ± 0.193 and 1.595 ± 0.43, p

The measurements of bio-signals are always subject to interference from noise, which would be able to affect the research results. In the present study, we introduce a technique to detect the signal quality by using ensemble empirical mode decomposition (EEMD) and Monte Carlo verification. We first decompose the original signals into several intrinsic mode functions (IMFs) and calculate the average distances between the signal IMFs and the negative (−1) slope line in Monte Carlo verification. Then, the approximate amount of white noise percent level in original signal could be obtained via corresponding to the created curve of distance and noise percent. This new proposed technique makes the approximate white noise percent level could be obtained much easier via a simple distance index through the EEMD and Monte Carlo verification methods.