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Julia C. Orri, Elizabeth M. Hughes, Deepa G. Mistry and Antone Scala

arrhythmic activity. Recently, there has been an increased focus on the addition of nonlinear HRV data, which have been shown to enhance risk assessment and overall available information of heart activity ( de Rezende Barbosa et al., 2018 ; Stein & Reddy, 2005 ). The Poincaré plot offers the researcher a

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Vinícius Y.B. Suetake, Emerson Franchini, Bruna T.C. Saraiva, Anne K.F. da Silva, Aline F.B. Bernardo, Rayane L. Gomes, Luiz Carlos M. Vanderlei and Diego G.D. Christofaro

quantitative and qualitative analyses of the Poincaré plot. The variables analyzed in the time domain were: I—the average of the RR intervals, II—the average SD of all normal RR intervals, and III—the square root of the mean squared differences between successive RR intervals (RMSSD) ( 42 ). The indices in the

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José Naranjo Orellana, Blanca de la Cruz Torres, Elena Sarabia Cachadiña, Moisés de Hoyo and Sergio Domínguez Cobo

Purpose:

The application of Poincaré-plot analysis to heart-rate variability (HRV) is a common method for the assessment of autonomic balance. However, results obtained from the indexes provided by this analysis tend to be difficult to interpret. In this study the authors aimed to prove the usefulness of 2 new indexes: the stress score (SS) and the sympathetic:parasympathetic ratio (S:PS ratio).

Methods:

25 professional Spanish soccer players from same team underwent 330 resting measurements of HRV. All subjects experienced 10 min of HRV monitoring through an R-R-interval recorder. The following parameters were calculated: (1) Poincaré-plot indexes: SD1 (transverse axis), which is proportional to parasympathetic activity; SD2 (longitudinal axis), which is inversely proportional to sympathetic activity; and the SD1:SD2 ratio; (2) time-domain parameters: standard deviation of R-R intervals (SDNN), root-mean-square differences of successive heartbeat intervals (rMSSD), and percentage of successive R-R-interval pairs differing in more than 50 ms in the entire recording divided by the total number of R-R intervals (pNN50); and (3) the proposed 2 new indexes: the SS and the S:PS ratio.

Results:

The study found a high negative correlation between the SS and SDNN (R 2 = .94). The S:PS ratio correlated inversely to rMSSD (R 2 = .95), SDNN (R 2 = .94), and pNN50 (R 2 = .74). The S:PS ratio showed a strong correlation with SD1 (R 2 = .95) and SS (r = .87, R 2 = .88).

Conclusions:

The application of the SS as sympathetic-activity index and the S:PS ratio as a representation of autonomic balance (SS:SD1) provides a better understanding of the Poincaré-plot method in HRV.

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Carla Cristiane Silva, Maurizio Bertollo, Felipe Fossati Reichert, Daniel Alexandre Boullosa and Fábio Yuzo Nakamura

Purpose:

To examine which body position and indices present better reliability of heart rate variability (HRV) measures in children and to compare the HRV analyzed in different body positions between sexes.

Method:

Twenty eutrophic prepubertal children of each sex participated in the study. The RR intervals were recorded using a portable heart rate monitor twice a day for 7 min in the supine, sitting, and standing positions. The reproducibility was analyzed using the intraclass correlation coefficient (ICC; two way mixed) and within-subject coefficient of variation (CV).Two-way ANOVA with repeated measures was used to compare the sexes.

Results:

High levels of reproducibility were indicated by higher ICC in the root-mean-square difference of successive normal RR intervals (RMSSD: 0.93 and 0.94) and Poincaré plot of the short-term RR interval variability (SD1: 0.92 and 0.94) parameters for boys and girls, respectively, in the supine position. The ICCs were lower in the sitting and standing positions for all HRV indices. In addition, the girls presented significantly higher values than the boys for SDNN and absolute high frequency (HF; p < .05) in the supine position.

Conclusions:

The supine position is the most reproducible for the HRV indices in both sexes, especially the vagal related indices.

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Reabias de A. Pereira, José Luiz de B. Alves, João Henrique da C. Silva, Matheus da S. Costa and Alexandre S. Silva

than 50 ms; rMSSD, root mean square of successive differences between RR intervals; SD1, SD of instantaneous beat-to-beat RR interval variability measured from Poincaré plots; SD2, SD of long-term beat-to-beat RR interval variability measured from Poincaré plots; SDNN, SD of RR intervals; SEE, standard

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Júlio A. Costa, João Brito, Fábio Y. Nakamura, Eduardo M. Oliveira and António N. Rebelo

HRV. The SWSE considers (1) the first 10 minutes of the first low and regular HR episode lasting at least 15 minutes; (2) the lowest SD of RR intervals (SD of normal to normal RR intervals [SDNN]) throughout the period of interest; (3) a round Poincaré plot (ie, a round cluster of points in RR

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Júlio A. Costa, João Brito, Fábio Y. Nakamura, Eduardo M. Oliveira, Ovidio P. Costa and António N. Rebelo

–R intervals. RR intervals were also used to produce the Poincaré plot SD 1 (representing the short-term beat-to-beat variability) and SD 2 (representing the long-term beat-to-beat variability) values. 33 Fast Fourier transform (Welch’s periodogram: 300-s window with 50% overlap) 32 was used to obtain

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Melissa G. Hunt, James Rushton, Elyse Shenberger and Sarah Murayama

.H. , Whykretowicz , A. , & Wysocki , H. ( 2007 ). Correlations between the Poincaré plot and conventional heart rate variability parameters assessed during paced breathing . The Journal of Physiological Sciences, 57 ( 1 ), 63 – 71 . PubMed doi:10.2170/physiolsci.RP005506 10.2170/physiolsci.RP005506

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Pedro L. Valenzuela, Guillermo Sánchez-Martínez, Elaia Torrontegi, Javier Vázquez-Carrión, Manuela González, Zigor Montalvo and Grégoire P. Millet

) was calculated as a measure of parasympathetic modulation. An analysis of the Poincaré plot was also performed to calculate the stress score (SS), a measure of sympathetic modulation, and the sympathetic:parasympathetic ratio (S:PS), an indicator of autonomic balance. 28 Statistical Analysis Data are

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novice marathon runners before and after marathon training. Sample entropy (SampEn) and Poincare plot SD1 and SD2 (PPSD1 and PPSD2) were calculated on respiratory time series data to characterize initial and post-training patterns. Methods: Students in a marathon training course (45 female, 23 male 19