Nonlinear dynamics of heart rate variability in response to orthostatism and hemodialysis in chronic renal failure patients: Recurrence analysis approach

González, Hortensia; Infante, Oscar; Pérez-Grovas, Héctor; José, Marco V.; Lerma, Claudia

doi:10.1016/j.medengphy.2012.04.013

Cited by 26 publications

(28 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Before HD the SDNN augments in response to orthostatism, whereas after HD the SDNN decreases in response to orthostatism. These examples are consistent with the response to orthostatism of the linear HRV indexes in healthy subjects and CRF patients, and with the response to HD reported before in these patients having stable hemodynamic variables during HD .…”

Section: Resultssupporting

confidence: 91%

See 1 more Smart Citation

Effects of Orthostatism and Hemodialysis on Mean Heart Period and Fractal Heart Rate Properties of Chronic Renal Failure Patients

Infante¹,

Pérez-Grovas²,

González

et al. 2017

Artificial Organs

Self Cite

View full text Add to dashboard Cite

The aim of this work was to evaluate the short-term fractal index (α ) of heart rate variability (HRV) in chronic renal failure (CRF) patients by identifying the effects of orthostatism and hemodialysis (HD), and by evaluating the correlation between α and the mean RR interval from sinus beats (meanNN). HRV time series were derived from ECG data of 19 CRF patients and 20 age-matched healthy subjects obtained at supine and orthostatic positions (lasting 5 min each). Data from CRF patients were collected before and after HD. α was calculated from each time series and compared by analysis of variance. Pearson's correlations between meanNN and α were calculated using the data from both positions by considering three groups: healthy subjects, CRF before HD and CRF after HD. At supine position, α of CRF patients after HD (1.17 ± 0.30) was larger (P < 0.05) than in healthy subjects (0.89 ± 0.28) but not before HD (1.10 ± 0.34). α increased (P < 0.05) in response to orthostatism in healthy subjects (1.29 ± 0.26) and CRF patients after HD (1.34 ± 0.31), but not before HD (1.25 ± 0.37). Whereas α was correlated (P < 0.05) with the meanNN of healthy subjects (r = -0.562) and CRF patients after HD (r = -0.388), no significance in CRF patients before HD was identified (r = 0.003). Multiple regression analysis confirmed that α was mainly predicted by the orthostatic position (in all groups) and meanNN (healthy subjects and patients after HD), showing no association with the renal disease condition in itself. In conclusion, as in healthy subjects, α of CRF patients correlates with meanNN after HD (indicating a more irregular-like HRV behavior at slower heart rates). This suggests that CRF patients with stable blood pressure preserve a regulatory adaptability despite a shifted setting point of the heart period (i.e., higher heart rate) in comparison with healthy subjects.

show abstract

Section: Resultssupporting

confidence: 91%

“…ECG recordings were obtained with a protocol described previously . Briefly, a continuous ECG recording (lead II) was obtained during 16 min in supine position, and a subsequent recording was also collected during 16 min of active orthostatism.…”

Section: Methodsmentioning

confidence: 99%

Effects of Orthostatism and Hemodialysis on Mean Heart Period and Fractal Heart Rate Properties of Chronic Renal Failure Patients

Infante¹,

Pérez-Grovas²,

González

et al. 2017

Artificial Organs

Self Cite

View full text Add to dashboard Cite

show abstract

“…Several methods have been developed to transform time series into networks for its analysis. Such methods include the visibility graphs method [8], and a plethora of its modifications [9,10], which consider the topological properties of the time series, the recurrence analysis of time series [11,12], and the analysis based on the phase space [13]. In this work, we usher in a new method to transform a time series into a network for its analysis, the   regular graphs.…”

Section: Introductionmentioning

confidence: 99%

Anticipation of ventricular tachyarrhythmias by a novel mathematical method: Further insights towards an early warning system in implantable cardioverter defibrillators

Zamudio

Márquez

José

2020

Preprint

View full text Add to dashboard Cite

BackgroundImplantable cardioverter defibrillators (ICD) are the most effective therapy to terminate malignant ventricular arrhythmias (VA) and therefore to prevent sudden cardiac death. Until today, there is no way to predict the onset of such VA. Our aim was to develop a mathematical model that could predict VA in a timely fashion. MethodsWe analyzed the time series of R-R intervals from 3 groups. Two groups from the Spontaneous Ventricular Tachyarrhythmia Database (v 1.0) were analyzed from a set of 81 pairs of R-R interval time series records from patients, each pair containing one record before the VT episode (Dataset 1A) and one control record which was obtained during the follow up visit (Dataset 1B). A third data set was composed of the RR interval time series of 54 subjects without a significant arrhythmia heart disease (Dataset 2). We developed a new method to transform a time series into a network for its analysis, the   regular graphs. This novel approach transforms a time series into a network which is sensitive to the quantitative properties of the time series, it has a single parameter ( )  to be adjusted, and it can trace long-range correlations. This procedure allows to use graph theory to extract the dynamics of any time series. ResultsThe average of the difference between the VT and the control record graph degree of each patient, at each time window, reached a global minimum value of 2.12  followed by a drastic increase of the average graph until reaching a local maximum of 5.59. The global minimum and the following local maxima occur at the windows 276 and 393, respectively. This change in the connectivity of the graphs distinguishes two distinct dynamics occurring during the VA, while the states in between the 276 and 393, determine a transitional state. We propose this change in the dynamic of the R-R intervals as a measurable and detectable "early warning" of the VT event, occurring an average of 514.625 seconds ( 8 : 30 minutes) before the onset of the VT episode. ConclusionsIt is feasible to detect retrospectively early warnings of the VA episode using their corresponding   regular graphs, with an average of 8 : 30 minutes before the ICD terminates the VA event.

show abstract

“…The objective of this work was to develop a pilot study where young healthy volunteer population was submitted to the physiological clino-orthostatic maneuver (COM) with the idea to generate steady/clinostatic and non-steady/orthostatic stages as a manner of an instrumental bench test (Gonzalez et al 2013; Cadena et al 2010). A hybrid calorimeter with the MC and the BbB techniques was applied to compare inter-individual variability changes during the COM stages.…”

Section: Introductionmentioning

confidence: 99%

VO2 and VCO2 variabilities through indirect calorimetry instrumentation

et al. 2013

View full text Add to dashboard Cite

The aim of this paper is to understand how to measure the VO2 and VCO2 variabilities in indirect calorimetry (IC) since we believe they can explain the high variation in the resting energy expenditure (REE) estimation. We propose that variabilities should be separately measured from the VO2 and VCO2 averages to understand technological differences among metabolic monitors when they estimate the REE. To prove this hypothesis the mixing chamber (MC) and the breath-by-breath (BbB) techniques measured the VO2 and VCO2 averages and their variabilities. Variances and power spectrum energies in the 0–0.5 Hertz band were measured to establish technique differences in steady and non-steady state. A hybrid calorimeter with both IC techniques studied a population of 15 volunteers that underwent the clino-orthostatic maneuver in order to produce the two physiological stages. The results showed that inter-individual VO2 and VCO2 variabilities measured as variances were negligible using the MC while variabilities measured as spectral energies using the BbB underwent 71 and 56% (p < 0.05), increase respectively. Additionally, the energy analysis showed an unexpected cyclic rhythm at 0.025 Hertz only during the orthostatic stage, which is new physiological information, not reported previusly. The VO2 and VCO2 inter-individual averages increased to 63 and 39% by the MC (p < 0.05) and 32 and 40% using the BbB (p < 0.1), respectively, without noticeable statistical differences among techniques. The conclusions are: (a) metabolic monitors should simultaneously include the MC and the BbB techniques to correctly interpret the steady or non-steady state variabilities effect in the REE estimation, (b) the MC is the appropriate technique to compute averages since it behaves as a low-pass filter that minimizes variances, (c) the BbB is the ideal technique to measure the variabilities since it can work as a high-pass filter to generate discrete time series able to accomplish spectral analysis, and (d) the new physiological information in the VO2 and VCO2 variabilities can help to understand why metabolic monitors with dissimilar IC techniques give different results in the REE estimation.

show abstract

Nonlinear dynamics of heart rate variability in response to orthostatism and hemodialysis in chronic renal failure patients: Recurrence analysis approach

Cited by 26 publications

References 46 publications

Effects of Orthostatism and Hemodialysis on Mean Heart Period and Fractal Heart Rate Properties of Chronic Renal Failure Patients

Effects of Orthostatism and Hemodialysis on Mean Heart Period and Fractal Heart Rate Properties of Chronic Renal Failure Patients

Anticipation of ventricular tachyarrhythmias by a novel mathematical method: Further insights towards an early warning system in implantable cardioverter defibrillators

VO2 and VCO2 variabilities through indirect calorimetry instrumentation

Contact Info

Product

Resources

About