A unified non-linear approach based on recurrence quantification analysis and approximate entropy: application to the classification of heart rate variability of age-stratified subjects

2018 ◽  
Vol 57 (3) ◽  
pp. 741-755 ◽  
Author(s):  
Vikramjit Singh ◽  
Amit Gupta ◽  
J. S. Sohal ◽  
Amritpal Singh
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Approximate Entropy ◽  
Recurrence Quantification Analysis ◽  
Linear Approach ◽  
Recurrence Quantification ◽  
Non Linear ◽  
Quantification Analysis

scholarly journals Possible Caveats of Ultra-Short Heart Rate Variability Reliability: Insights from Recurrence Quantification Analysis

2021 ◽  
Author(s):  
Hiago Murilo Melo ◽  
Mariana Cardoso Melo ◽  
Roger Walz ◽  
Emílio Takase ◽  
Jean Faber
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Recurrence Quantification Analysis ◽  
Dynamic Information ◽  
Recurrence Quantification ◽  
Quantification Analysis ◽  
The Difference ◽  
Main Components ◽  
Clinical Measures

Abstract The heart rate variability (HRV) is the difference between consecutive R-R intervals of heartbeats measured in milliseconds. HRV indices represent the role of sympathetic and parasympathetic autonomic branches. Even though HRV is considered an indirect biomarker of Autonomic Nervous System, there are not yet standardized protocols providing reliable clinical measures. One of the reasons is because HRV techniques requires long recording periods. There are attempts of decreasing the required recording, such as the strategy of ultra-short HVR recording (< one minute), which could make the utilization of the technique easier. However, there is little published about its reliability. This work proposes a method to evaluate the reliability of ultra-short HVR based in Poincare map and Recurrence Quantification Analysis, well known methods to assess nonlinear and dynamic information from a system, in order to verify the reliability of the use of ultra-short term HRV. Then, these results was compared with the classical HRV coefficients, such as rMSSD, recorded from subjects in spontaneous breathing and also, in controlled breathing protocols. As a conclusion, using the proposed methods, we were able to show the discrepancy between the segments of interest, both on mean and in variance, explained in the analysis of main components.

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