Open AccessHeart Rate Variability Assessment by the Lyapunov Exponent
Author(s) -
Prof. Jae Mok Ahn*
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.h6751.069820
Subject(s) - sample entropy , lyapunov exponent , approximate entropy , heart rate variability , mathematics , statistics , entropy (arrow of time) , rr interval , heart rate , physics , time series , computer science , medicine , artificial intelligence , chaotic , quantum mechanics , blood pressure
Heart rate variability (HRV) is a measure that evaluates cardiac autonomic activity according to the complexity or irregularity of an HRV dataset. At present, among various entropy estimates, the Lyapunov exponent (LE) is not as well described as approximate entropy (ApEn) and sample entropy (SampEn). Therefore, in this study, we investigated the characteristics of the parameters associated with the LE to evaluate whether the LE parameters can replace the frequency-domain parameters for HRV analysis. For the LE analysis in this study, two-dimensional factors were adjusted: length, which determines the size of the dimension vectors and is known as time delay embedding, varied over a range of 1 to 7, and the interval, which determines the distance between two successive embedding vectors, varied over a range of 1 to 3. A new parameter similar to the LA, the accumulation of the LE, was developed along with the LE to characterize the HRV parameters. The high frequency (HF) components dominated when the mean value of the LA was largest for interval 2, with 2.89 ms2 at the low frequency (LF) and 4.32 ms2 at the HF. The root mean square of the successive difference (RMSSD) in the LE decreased with increasing length in interval 1 from 2.6056 for length 1 to 0.2666 for length 7, resulting in a low HRV. The results suggest that the Lyapunov exponent methodology could be used in characterizing HRV analysis and replace power spectral estimates, specifically, HF components.