Why the study?
The study was conducted to evaluate whether ultra-short time series of RR-intervals analyzed through default chaotic global techniques could discriminate obese youths from non-obese youths.
Does chaotic global analysis of ultra-short RR-intervals discriminate autonomic dysfunction in obese youths compared to non-obese youths?
Does chaotic global analysis of ultra-short RR-intervals discriminate autonomic dysfunction in obese youths compared to non-obese youths?
Chaotic global analysis of ultra-short RR-interval time series is a robust technique to distinguish autonomic dysfunction in obese youths without the need for cubic spline interpolations.
May support autonomic screening in obese youth; leaves open diagnostic utility pending validation.
BACKGROUND: In this study, we applied ultra-short time series of interbeat intervals (RR-intervals) to evaluate heart rate variability through default chaotic global techniques with the purpose of discriminating obese youths from non-obese youth patients. METHOD: Chaotic global analysis of the RR-intervals from the electrocardiogram and pre-processing adjustments was undertaken. The effect of cubic spline interpolations was assessed, while the spectral parameters remained fixed. Exactly, 125 RR-intervals of data were recorded. RESULTS: CFP1, CFP3, and CFP6 were the only significant combinations of chaotic globals when the standard conditions were enforced and at the level p<0.01 (or <1%). These significances were acheived via Kruskal-Wallis and Cohen's ds effects sizes tests of significance after Anderson-Darling and Lilliefors statistical tests indicated non-normal distributions in the majority of cases. Adjustments of the cubic spline interpolation from 1 to 13 Hz were revealed to be inconsequential when measured by Kruskal-Wallis and Cohen's ds, regarding the outcome between the two datasets. CONCLUSION: Chaotic global analysis was offered as a robust technique to distinguish autonomic dysfunction in obese youths. It can discriminate the two different groups using ultra-short data lengths, and no cubic spline interpolations need be applied.
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Garner et al. (2019) studied this question.
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