Objectives The purpose of our study was to compare three definitions of ambulatory blood pressure (BP) nocturnal period and to assess their agreement in determining nocturnal BP dipping patterns. Methods We investigated 69 subjects with metabolic syndrome, aged 50–55 years. In all subjects, we assessed 24-h BP monitoring, electrocardiogram and actigraphy profiles. The nocturnal period was defined in three ways: as a fixed narrow nighttime period from 01:00 to 06:00, as a self-reported sleeping period and as a disappearance and onset of physical activity recorded by the actigraph. Results Our study revealed a significant discrepancy between the self-reported and actigraphy-based nocturnal periods (P < 0.001). In addition, different definitions of the nighttime yielded significant differences in determining nondipping, extreme dipping and dipping BP patterns, the identification of the latter being affected the most. The actigraphy-based approach best aligned with the fixed-time determination of the nocturnal period: Cohen’s kappa coefficient for the nondipping pattern was 0.78 (0.58–1), for the dipping pattern 0.75 (0.59–0.91) and for the extreme dipping pattern 0.81 (0.65–0.97). In comparison to the self-reported determination of the nocturnal period, using the actigraphy-based approach resulted in reclassifying the nocturnal BP pattern in 20.3% of subjects. Conclusions The lack of agreement between fixed-time, self-reported and actigraphy-based determinations of the nighttime period affects the identification of the nocturnal BP patterns. In comparison to the self-reported nocturnal period estimation, the actigraphy-based approach results in the reclassification of BP dipping status in every fifth subject.

Background and Objectives: The available research shows conflicting data on the heart rate variability (HRV) in metabolic syndrome (MetS) subjects. The discrepancy suggests a methodical shortcoming: due to the influence of physical activity, the standard measuring of HRV at rest is not comparable with HRV assessment based on 24 h Holter monitoring, which is preferred because of its comprehensiveness. To obtain a more reliable measure and to clarify to what extent HRV is altered in MetS, we assessed a 24 h HRV before and after the elimination of the influence of physical activity. Materials and Methods: We investigated 69 metabolic syndrome (MetS) and 37 control subjects, aged 50–55. In all subjects, 24 h monitoring of electrocardiogram, blood pressure, and actigraphy profiles were conducted. To eliminate the influence of day-time physical activity on RR intervals (RRI), a linear polynomial autoregressive model with exogenous terms (ARX) was used. Standard spectral RRI analysis was performed. Results: Subjects with MetS had blunted HRV; the diurnal SDNN index was reliably lower in the MetS group than in control subjects. The elimination of the influence of physical activity did not reveal a significant HRV change in long-term indices (SDNN, SDANN, and SD2), whilst adjacent RRI values (RMSSD, pNN50, and SD1) and SDNN index significantly increased (p < 0.001). An increase in the latter indices highlighted the HRV difference between the MetS and control groups; a significant (p < 0.001) decrease of all short-term HRV variables was found in the MetS group (p < 0.01), and low-frequency spectral components were less pronounced in the MetS group. Conclusion: The application of a polynomial autoregressive model in 24 h HRV assessment allowed for the exclusion of the influence of physical activity and revealed that MetS is associated with blunted HRV, which reflects mitigated parasympathetic tone.

As heart rate variability (HRV) studies become more and more prevalent in clinical practice, one of the most common and significant causes of errors is associated with distorted RR interval (RRI) data acquisition. The nature of such artifacts can be both mechanical as well as software based. Various currently used noise elimination in RRI sequences methods use filtering algorithms that eliminate artifacts without taking into account the fact that the whole RRI sequence time cannot be shortened or lengthened. Keeping that in mind, we aimed to develop an artifacts elimination algorithm suited to long-term (hours or days) sequences that does not affect the overall structure of the RRI sequence and does not alter the duration of data registration. An original adaptive smart time series step-by-step analysis and statistical verification methods were used. The adaptive algorithm was designed to maximize the reconstruction of the heart-rate structure and is suitable for use, especially in polygraphy. The authors submit the scheme and program for use.