The development of poincare plot in the analysis of heart rate variability
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Background Heart Rate Variability is an objective index to assess cardiac autonomic function. Time-domain,frequency-domain and nonlinear analysis are the most important ways to analyze the heart rate variability. Currently,the nonlinear analysis method is becoming more and more popular.Objective Introducing the scatterplot method in the analysis of heart rate variability and its potential value in the clinic.Content The development of heart rate variability of the process,mainly various scatter plot analysis and its clinical application.Trend Scatter plot is expected as a more comprehensive and real analysis method to reflect heart rate variability,then make good sense clinically.
Key words:
Heart rate variability; Poincare plot; Clinical applicationKeywords:
Poincaré plot
Plot (graphics)
Scatter plot
Autonomic function
Recurrence plot
Due to the proven efficiency of heart rate variability analysis as a non invasive method in the study of certain pathological and physiological states and given the multiplicity of the methods used by different authors, in this work we intend to compare two of the simplest and most efficient of them.
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Abstract The assessment of heart rate (HR) and heart rate variability (HRV) based on electrocardiograms (ECG) is considered a good proxy for stress in a wide range of animal species. However, problems can occur e.g., when measuring ECG in ambulatory settings such as recording of unrestrained animals using non‐invasive devices. Artefacts caused by technical (i.e. bad electrode contact) or physiological (i.e. ectopic beats, non‐cardiac muscle potentials) sources are common and can disturb the ECG signal. As HRV analysis is highly sensitive to artefacts in the interbeat interval (RR‐interval) time series the process of visual inspection of the raw signal to detect and correct these is essential. Most of the commercially available software requires intensive training and extensive manual work to accomplish this task and/or is often not available to access for free. EasieRR is an open‐source, stand‐alone software optimized for analysing ECG in non‐restrained animals. The program allows a species‐specific analysis and calculation of recommended standard HRV parameters in both, the time‐ and the nonlinear domain (RMSSD, SDNN, SD1 and SD2). Visualization of data using Poincaré plots and tachograms of RR‐intervals eases the validation of correct heart cycle interval detection and minimizes manual work for the user. Automatically detected peaks can be manually corrected via deletion, correction of spurious detections or marking of undetected peaks. The HRV analysis can be exported using common formats (TXT, MAT). Figures can be plotted and exported in various formats (PDF, SVG, PNG, JPG, TIFF, EMF and EPS). Included in EasieRR is the possibility for synchronization of ECG data with video in order to link cardiac responses to specific behavioural responses.
Spurious relationship
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Poincaré plot
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Recurrence plot
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The Poincaré plot is a geometrical technique used to visualise and quantify the correlation between two consecutive data points in a time-series. Since the dynamics of fluctuations in physiological rhythms exhibit long-term correlation and memory, this study aimed to extend the Poincaré plot by calculating the correlation between sequential data points in a time-series, rather than between two consecutive points. By incorporating this so-called lag, we hope to integrate a temporal aspect into quantifying the correlation, to depict whether a physiological system holds prolonged association between events separated by time. In doing so, it attempts to instantaneously characterise the intrinsic behaviour of a complex system. We tested this hypothesis on three different physiological time-series: heart rate variability in patients with liver cirrhosis, respiratory rhythm in asthma and body temperature fluctuation in patients with cirrhosis, to evaluate the potential application of the extended Poincaré method in clinical practice. When studying the cardiac inter-beat intervals, the extended Poincaré plot revealed a stronger autocorrelation for patients with decompensated liver cirrhosis compared to less severe cases using Pearson's correlation coefficient. In addition, long-term variability (known as SD2 in the extended Poincaré plot) appeared as an independent prognostic variable. This holds significance by acting as a non-invasive tool to evaluate patients with chronic liver disease and potentially facilitate transplant selection as an adjuvant to traditional criteria. For asthmatics, employing the extended Poincaré plot allowed for a non-invasive tool to differentially diagnose various classifications of respiratory disease. In the respiratory inter-breath interval analysis, the receiver operating characteristic curve (ROC) provided evidence that the extension of the Poincaré plot holds a greater advantage in the classification of asthmatic patients, over the traditional Poincaré plot. Lastly, the analysis of body temperature from patients using the extended Poincaré plot helped identify inpatients from outpatients with cirrhosis. Through these analyses, the extended Poincaré plot provided unique and additional information which could potentially make a difference in clinical practice. Conclusively, the potential use of our work lies in its possible application of predicting mortality for the organ allocation procedure in patients with cirrhosis and non-invasively distinguish between atopic and non-atopic asthma.
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Recurrence plot
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Most of the currently accepted approaches to compute heart rate and assess heart rate variability operate on interpolated, continuous-valued heart rate signals, thereby ignoring the underlying discrete structure of human heart beats. To overcome this limitation, we model the stochastic structure of heart beat intervals as a history-dependent, inverse Gaussian process and derive from it an explicit probability density describing heart rate and heart rate variability. We estimate the parameters of the inverse Gaussian model by local maximum likelihood and assess model goodness-of-fit using Q-Q plot analyses. We apply our model in an analysis of human heart beat intervals from a tilt-table experiment. Our results suggest that the new definitions of heart rate and heart rate variability convey different information than other conventional indices, both in time and frequency domains, and may have important implications for research studies of cardiovascular and autonomic regulation.
Inverse Gaussian distribution
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Human heart
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A novel descriptor (Complex Correlation Measure (CCM)) for measuring the variability in the temporal structure of Poincaré plot has been developed to characterize or distinguish between Poincaré plots with similar shapes.This study was designed to assess the changes in temporal structure of the Poincaré plot using CCM during atropine infusion, 70° head-up tilt and scopolamine administration in healthy human subjects. CCM quantifies the point-to-point variation of the signal rather than gross description of the Poincaré plot. The physiological relevance of CCM was demonstrated by comparing the changes in CCM values with autonomic perturbation during all phases of the experiment. The sensitivities of short term variability (SD1), long term variability (SD2) and variability in temporal structure (CCM) were analyzed by changing the temporal structure by shuffling the sequences of points of the Poincaré plot. Surrogate analysis was used to show CCM as a measure of changes in temporal structure rather than random noise and sensitivity of CCM with changes in parasympathetic activity.CCM was found to be most sensitive to changes in temporal structure of the Poincaré plot as compared to SD1 and SD2. The values of all descriptors decreased with decrease in parasympathetic activity during atropine infusion and 70° head-up tilt phase. In contrast, values of all descriptors increased with increase in parasympathetic activity during scopolamine administration.The concordant reduction and enhancement in CCM values with parasympathetic activity indicates that the temporal variability of Poincaré plot is modulated by the parasympathetic activity which correlates with changes in CCM values. CCM is more sensitive than SD1 and SD2 to changes of parasympathetic activity.
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Dynamics
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The Poincare plot is an emerging Heart Rate Variability (HRV) analysis technique, the geometry of which has been shown to distinguish between healthy and unhealthy subjects in clinical settings. The Poincare plot is able to display nonlinear aspects of the interval sequence and is therefore of interest in characterizing the nonlinear aspects of HRV. The problem is, how do we quantitatively characterize the geometry of the plot to capture useful descriptors that are independent of existing HRV measures? In this paper, we investigate a popular existing category of techniques and show that they measure linear aspects of the intervals which existing HRV indices already specify. The fact that these methods appear insensitive to the nonlinear characteristics of the intervals is an important finding because the Poincare plot is primarily a nonlinear technique.
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Poincaré conjecture
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