scholarly journals Nonlinear Methods Most Applied to Heart-Rate Time Series: A Review

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 309 ◽  
Author(s):  
Teresa Henriques ◽  
Maria Ribeiro ◽  
Andreia Teixeira ◽  
Luísa Castro ◽  
Luís Antunes ◽  
...  
Keyword(s):  
Heart Rate ◽  
Recurrence Plot ◽  
Multiscale Entropy ◽  
Fluctuation Analysis ◽  
Mathematical Methods ◽  
Poincaré Plot ◽  
Nonlinear Methods ◽  
Plot Analysis ◽  
Heart Rate Dynamics ◽  
Recurrence Plot Analysis

The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.

scholarly journals A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Suraj K. Nayak ◽  
Arindam Bit ◽  
Anilesh Dey ◽  
Biswajit Mohapatra ◽  
Kunal Pal
Keyword(s):  
Signal Analysis ◽  
System Analysis ◽  
Nonlinear Dynamical System ◽  
Approximate Entropy ◽  
Recurrence Plot ◽  
Fluctuation Analysis ◽  
Ecg Signal ◽  
Poincaré Plot ◽  
Nonlinear Methods ◽  
Nonlinear Dynamical

Electrocardiogram (ECG) signal analysis has received special attention of the researchers in the recent past because of its ability to divulge crucial information about the electrophysiology of the heart and the autonomic nervous system activity in a noninvasive manner. Analysis of the ECG signals has been explored using both linear and nonlinear methods. However, the nonlinear methods of ECG signal analysis are gaining popularity because of their robustness in feature extraction and classification. The current study presents a review of the nonlinear signal analysis methods, namely, reconstructed phase space analysis, Lyapunov exponents, correlation dimension, detrended fluctuation analysis (DFA), recurrence plot, Poincaré plot, approximate entropy, and sample entropy along with their recent applications in the ECG signal analysis.

scholarly journals Validation of the Polar RS800CX for assessing heart rate variability during rest, moderate cycling and post-exercise recovery

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1501 ◽  
Author(s):  
Kyriakos I. Tsitoglou ◽  
Yiannis Koutedakis ◽  
Petros C. Dinas
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Recurrence Plot ◽  
Maximal Heart Rate ◽  
Poincaré Plot ◽  
Post Exercise ◽  
Plot Analysis ◽  
Mean Differences ◽  
Post Exercise Recovery ◽  
Electrocardiogram Ecg

Background: Heart rate variability (HRV) is an autonomic nervous system marker that provides reliable information for both disease prevention and diagnosis; it is also used in sport settings. We examined the validity of the Polar RS800CX heart rate monitor during rest, moderate cycling, and recovery in considering the total of 24 HRV indices. Method: A total of 32 healthy males (age=24.78±6.87 years, body mass index=24.48±3.13 kg/m2) completed a session comprised by three 20-minute time periods of resting, cycling at 60% of maximal heart rate, and recovery using a Polar RS800CX and an electrocardiogram (ECG) monitors. The HRV indices included time-domain, frequency-domain, Poincaré plot and recurrence plot. Bland–Altman plot analysis was used to estimate agreement between Polar RS800CX and ECG. Results: We detected significant associations (r>0.75, p<0.05) in all HRV indices, while five out of 24 HRV indices displayed significant mean differences (p<0.05) between Polar RS800CX and ECG during the resting period. However, for the exercise and recovery periods, we found significant mean differences (p<0.05) in 16/24 and 22/24 HRV indices between the two monitors, respectively. Conclusion: It is concluded that Polar RS800CX is a valid tool for monitoring HRV in individuals at resting conditions, but it displays inconsistency when used during exercise at 60% of maximal heart rate and recovery periods.

NONLINEAR INDICES OF HEART RATE VARIABILITY FOR DIFFERENTIATING ARRHYTHMIAS

2013 ◽  
Vol 13 (04) ◽  
pp. 1350061 ◽  
Author(s):  
N. D. ASHA ◽  
K. PAUL JOSEPH
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Normal Sinus Rhythm ◽  
Ventricular Tachyarrhythmia ◽  
Recurrence Plot ◽  
Largest Lyapunov Exponent ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Poincaré Plot ◽  
Diagonal Length

Heart rate variability (HRV) is the temporal variation between sequences of consecutive heartbeats. Chaos and fractal-based measurements have been widely used for quantifying the HRV for cardiac risk stratification purposes. In this paper, five different sets of HRVs, viz., normal sinus rhythm (NSR), congestive heart failure (CHF), cardiac arrhythmia suppression trial (CAST), supra ventricular tachyarrhythmia (SVTA) and atrial fibrillation (AF), have been analysed using nonlinear parameters to fix the ranges of each parameter. Data were downloaded from the PhysioNet database with 15 sets in each case. The parameters used for analysis were Poincare plot measures: SD1, SD2 and SD12, largest Lyapunov exponent (LLE), correlation dimension (CD); recurrence plot measures: recurrence rate (REC), determinism (DET), mean diagonal length (L mean ), maximal diagonal length (L max ) and entropy (ENTR); detrended fluctuation analysis measures: scaling exponent (α) and fractal dimension (FD); sample entropy (SampEn); and approximate entropy (ApEn). Analysis of variance (ANOVA) was done for confirming the differences in parameter values between various cases. All parameters except LLE showed a significant statistical difference for different cases.

Fetal heart rate variability study with recurrence plot analysis

2013 ◽  
Author(s):  
Bogdan Hurezeanu ◽  
G. Mihaela Ungureanu ◽  
Angela Digulescu ◽  
Alexandru Serbanescu ◽  
Hariton Costin ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Recurrence Plot ◽  
Plot Analysis ◽  
Fetal Heart Rate Variability ◽  
Recurrence Plot Analysis ◽  
Variability Study

Characterization of HRV by Poincare Plot Analysis among the Female Tea Garden Workers of Northern Hilly Regions of West Bengal

2012 ◽  
pp. 206-215
Author(s):  
Somsirsa Chatterjee ◽  
Ankur Ganguly ◽  
Saugat Bhattacharya
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
West Bengal ◽  
Mean Value ◽  
Short Term ◽  
Poincaré Plot ◽  
Plot Analysis ◽  
Tea Garden

Recent research on Heart Rate Variability (HRV) has proven that Poincare Plot is a powerful tool to mark Short Term and Long Term Heart Rate Variability. This study focuses a comprehensive characterization of HRV among the Tea Garden Workers of the Northern Hilly Regions of West Bengal. The characterization, as available from the data sets, projects the average values of SD1 characteristics, that is, Short Term HRV in females as 58.265ms and SD2 as 149.474. The SDRR shows a mean value of 87.298 with a standard deviation of 119.669 and the S Characterization as 16505.99 ms and Standard deviation of 45882.31 ms. The SDRR shows a mean value of 87.298 with a standard deviation of 119.669 and the S Characterization as 16505.99 ms and Standard deviation of 45882.31 ms. ApEn Characterization showed mean value of 0.961 and standard deviation of 0.274.

Linear and Fractal Heart Rate Dynamics during Sleep at High Altitude

2010 ◽  
Vol 49 (05) ◽  
pp. 521-525 ◽  
Author(s):  
P. Castiglioni ◽  
F. Rizzo ◽  
A. Faini ◽  
P. Mazzoleni ◽  
C. Lombardi ◽  
...  
Keyword(s):  
Heart Rate ◽  
High Altitude ◽  
Cardiac Response ◽  
Fluctuation Analysis ◽  
Similarity Coefficients ◽  
Mount Everest ◽  
Heart Rate Dynamics ◽  
Biological Interpretation ◽  
Additional Support ◽  
Cardiac Variability

Summary Objectives: To investigate the effects of hypoxia during sleep on linear and self-similar components of heart rate variability (HRV) in eight healthy subjects at high altitude on Mount Everest. Methods: ECG was monitored by using an innovative textile-based device, the MagIC system. For each subject three night recordings were performed at sea level (SL), at 3500 m and 5400 m above SL. RR Interval (RRI) was derived on a beat-by-beat basis from the ECG and the VLF, LF and HF spectral components and the LF/HF ratio were estimated. Short-(α1) and long-term (α2) scale exponents as well as the recently proposed spectrum of self-similarity coefficients, α(n) were estimated by detrended fluctuation analysis (DFA). Results: With respect to SL, all HRV parameters but one (α2) were significantly modified at 3500 m. However, at 5400 m they tended to return to the SL values and this was in contrast with the increase in the hypobaric hypoxia and in the number of central sleep apneas occurring at higher altitude. The only HRV index that displayed changes at 5400 m was the DFA α(n) spectrum, with α(n) values significantly lower than at SL for 20 < n < 50 and higher for 200 < n < 400, being n the box size.. Conclusions: While the biological interpretation of these results is still in progress, our data indicates that the cardiac response to high altitude hypoxia during sleep can hardly be fully explored by traditional HRV estimators only, and requires the additional support of more sophisticated indexes exploring also nonlinear and fractal features of cardiac variability.

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