Complexity-Based Decoding of the Coupling Among Heart Rate Variability (HRV) and Walking Path

Kamal, Shahul Mujib; Babini, Mohammad Hossein; Krejcar, Ondřej; Namazi, Hamidreza

doi:10.3389/fphys.2020.602027

Cited by 26 publications

(4 citation statements)

References 28 publications

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“…Consequently, the dynamics of spontaneous healthy brain activity are complex and contain information with fractal-like patterns that are self-similar over multiple scales of time 33 . In addition, Kamal and colleagues strengthened this concept by showing that healthy variable patterns are present in the brain and heart when participants walk on a fractal-like path 10,11 . Thus, consider additional biological rhythms will strengthen the multilevel dynamic interaction hypothesis.…”

Section: Discussionmentioning

confidence: 99%

“…demonstrate similar patterns in their temporal structures. Recent preliminary research has showed such patterns to be present in the brain and heart when the individuals walked on a fractal-like path 10,11 and the brain when they finger-thumb tapped coordinating with an auditory fractal-like stimulus 12 . These results suggest an interaction between elements and systems at the temporal structure domain.…”

mentioning

confidence: 99%

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Muscle activity variability patterns and stride to stride fluctuations of older adults are positively correlated during walking

Jordão,

Stergiou,

Brandão

et al. 2023

Sci Rep

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It has been found that fractal-like patterns are present in the temporal structure of the variability of healthy biological rhythms, while pathology and disease lead to their deterioration. Interestingly, it has recently been suggested that these patterns in biological rhythms are related with each other, reflecting overall health or lack of it, due to their interaction. However, the underlying neurophysiological mechanisms responsible for such dependency remain unknown. In addition, this relationship between different elements needs to be first verified before we even pursue understanding their interaction. This study aimed to investigate the relationship between two elements of the neuromuscular system, gait and muscle activity variability patterns in older adults. Twenty-one older adults walked at their preferred walking speed on a treadmill. Inter-stride intervals were obtained through an accelerometer placed on the lateral malleoli to assess the temporal structure of variability of stride-to-stride fluctuations. Inter muscle peak intervals were obtained through the electromyographic signal of the gastrocnemius to assess the temporal structure of the variability of the simultaneous muscle activity. The temporal structure of variability from both signals was evaluated through the detrended fluctuation analysis, while their magnitude of variability was evaluated using the coefficient of variation. The Pearson’s Correlation coefficient was used to identify the relationship between the two dependent variables. A significant strong positive correlation was found between the temporal structure of gait and muscle activity patterns. This result suggests that there is an interdependency between biological rhythms that compose the human neuromuscular system.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Muscle activity variability patterns and stride to stride fluctuations of older adults are positively correlated during walking

Jordão,

Stergiou,

Brandão

et al. 2023

Sci Rep

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“…However, the exact mechanism regulating HRV is not perfectly clear in every detail (Ernst, 2017;Rajendra Acharya et al, 2006). Some of recent developments in HRV have been mainly focused on nonlinear metrics such as variants of approximate entropy (ApEn) and sample entropy (SampEn) (Kamal et al, 2020;Lee & Choi, 2018;C. Liu et al, 2013;Sassi et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

HRnV-Calc: A Software for Heart Rate n-Variability and Heart Rate Variability Analysis

Niu,

Guo,

Ong

et al. 2023

JOSS

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“…SampEn can be used to describe the regularity of temporal series, as it has lower values for time series with more frequent occurrence of sequences of similar consecutive values. Sample entropy method was used in physiology [51,52], geophysics [53], climatology [54], hydrology [30,34,35], and engineering [55].…”

Section: Sample Entropy (Sampen)mentioning

confidence: 99%

Multiscale Complexity Analysis of Rainfall in Northeast Brazil

Silva

Barreto

Cunha-Filho

et al. 2021

Water

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In this work, we analyze the complexity of monthly rainfall temporal series recorded from 1962 to 2012, at 133 gauge stations in the state of Pernambuco, northeastern Brazil. To this end, we employ the modified multiscale entropy method (MMSE), which is well suited for short time series, to analyze the rainfall regularity across a wide range of temporal scales, from one month to one year. We identify the temporal scales that distinguish rainfall regularity in the inland semiarid Sertão region, the transitional inland Agreste region, and the coastal, tropical humid Zona da Mata region, by comparing the results for stations across the study area and performing statistical significance tests. Our work contributes to the establishment of multiscale methods based on information theory in climatological studies.

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Complexity-Based Decoding of the Coupling Among Heart Rate Variability (HRV) and Walking Path

Cited by 26 publications

References 28 publications

Muscle activity variability patterns and stride to stride fluctuations of older adults are positively correlated during walking

Muscle activity variability patterns and stride to stride fluctuations of older adults are positively correlated during walking

HRnV-Calc: A Software for Heart Rate n-Variability and Heart Rate Variability Analysis

Multiscale Complexity Analysis of Rainfall in Northeast Brazil

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