Poincare indices for analyzing meditative heart rate signals

Goshvarpour, Ateke; Goshvarpour, Atefeh

doi:10.4103/2319-4170.143528

Cited by 18 publications

(6 citation statements)

References 20 publications

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“…These basic descriptors for analysing Poincaré plots have been largely studied. They were used to examine temporal variability of RR intervals (Karmakar et al ., , ), changes in type 2 diabetic patients (Roy & Ghatak, ) and heart rate signals during meditation (Goshvarpour & Goshvarpour, ). For instance, the area of the ellipse on Poincaré plots in atrial fibrillation was inversely related with rhythm rate, and this area grew significantly while heart rate decreased.…”

Section: Discussionmentioning

confidence: 99%

Geometric patterns of time‐delay plots from different cardiac rhythms and arrhythmias using short‐term EKG signals

Borracci

Pulvet

Ingino³

et al. 2017

Clin Physio Funct Imaging

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Modified Poincaré plots with smoothed lines connecting successive points could accurately classify different types of arrhythmias based on short RR interval sequence variability. Characteristic emergent patterns can be visually identified and eventually could be distinguished by an automatic classification system able to discern between arrhythmias. This work provides an alternative method to interpret time-delay plots obtained from short-term EKG signal recordings.

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Section: Discussionmentioning

confidence: 99%

Geometric patterns of time‐delay plots from different cardiac rhythms and arrhythmias using short‐term EKG signals

Borracci

Pulvet

Ingino³

et al. 2017

Clin Physio Funct Imaging

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“…More SD represents less self-similarity in the data set. [212223] SD1 and SD2 are defined in Eqs. 1 and 2:…”

Section: Methodsmentioning

confidence: 99%

Force variability in the short- and long-term type 2 diabetes mellitus

et al. 2019

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Background:Force variability is related to many kinesiological and neuromuscular properties of the body. This study was conducted to evaluate the effect of type 2 diabetes mellitus (T2DM) and sex on the several fractal and entropy indices of force changing during the repetitive isokinetic exercise of knee flexion-extension.Methods:Fifty individuals were allowed to participate in the study, and they consist of 18 patients with short-term T2DM, 12 patients with long-term T2DM, and 20 gender/body mass index/ankle imposed to brachial pressure index and physical activity index-matched healthy control (HC) individuals. Torque of knee flexion-extension was recorded for each cycle of 40 isokinetic repetitions at a velocity of 150°/s. The slope across the peak of torques and nonlinear fractal and entropy features in the time series was calculated. Two-way univariate analysis of variance was used to analyze the effect of the groups and gender on the variables.Results:The slope of flexor peak torques was significantly less in the long-term T2DM than the other groups. However, the fractal features such as SD1 and 2 of Poincare plot and fractal dimension katz were significantly decreased in the T2DM groups than the HC and in the women than men. Alpha detrended fluctuation analysis and empirical hurts exponent increased in women of short-term T2DM than men.Conclusion:The force variability decreased in the T2DM as compared to HC and in women as compared to men. However, the randomness of force was significantly increased in women of short-term T2DM.

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“…HRV provides useful information that can quantify the activity of the autonomic nervous system (ANS) by means of power spectral estimates and nonlinear estimates, which consist of the parasympathetic nervous system (PNS) and sympathetic nervous system (SNS) [1][2][3][4]. A nonlinear diagnostic parameter that can be obtained through a Poincare plot analysis is the result of a graphical representation of the difference between consecutive normal-to-normal (NN) heartbeat intervals in a time series in milliseconds [5][6][7][8]. This graphical expression includes the shape, size, dispersion, position, clusters, and localization for all the HRV data points on the Cartesian coordinate system.…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Variablity Assessment using the Poincare Plot Parameters during Sleep

Ahn¹

2020

International Journal of Engineering Research and Technology

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A nonlinear Poincare plot method for heart rate variability (HRV) analysis is widely used to evaluate autonomic nervous system activity. However, during sleep, the Poincare plot parameters are not described completely because an ectopic heartbeat, which is similar to an artifact, depends largely on the result of the Poincare plot. Therefore, in this study, we investigated the characteristics of four Poincare plot parameters to differentiate between rapid eye movement (REM) and nonrapid eye movement (NREM) after the removal of any artifacts. For the sleep Poincare plot analysis, two 10 min target HRV segments were obtained for each of the REM and NREM sleep stages from 16 individual subjects without autonomic disease. With a 2 min moving window with a time shift of 2 s, 240 results were created and led to the formation of cloud points (or ellipses) on the Cartesian coordinate system. The length (SD1) and width (SD2) of an ellipse, ellipse area (Ln sArea), and SD2/SD1 ratio were calculated to obtain the mean value and standard deviation (SD) of all the results. The SD2/SD1 ratio and Ln sArea exhibited excellent discrimination ability between REM and NREM in terms of their mean and SD (p = 0.0001 and p = 0.0003, respectively; 95% CI), but SD1 did not (p = 0.9606 for mean and p = 0.0320 for SD). The Ln sArea remained nearly constant and was independent of the sleep stages. The results indicate that the Ln sArea and SD2/SD1 ratio could be used to assess physiological changes and health status during sleep and to discriminate between REM and NREM, respectively.

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Poincare indices for analyzing meditative heart rate signals

Cited by 18 publications

References 20 publications

Geometric patterns of time‐delay plots from different cardiac rhythms and arrhythmias using short‐term EKG signals

Geometric patterns of time‐delay plots from different cardiac rhythms and arrhythmias using short‐term EKG signals

Force variability in the short- and long-term type 2 diabetes mellitus

Heart Rate Variablity Assessment using the Poincare Plot Parameters during Sleep

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