Abstract
The beat-to-beat fluctuations in the electrocardiogram (ECG) are called heart rate variability (HRV). There are various linear and nonlinear methods for measurement of the HRV. Poincare plot is one of the nonlinear methods to evaluate HRV. Previous studies have shown strong correlation between Poincare plot descriptors SD1 and SD2 with linear and nonlinear HRV measures. The present study is aimed to verify the correlation during fasting and postprandial states. A total of 46 young adults participated in the study. 10 min of ECG lead II was recorded in supine position with minimum 8 h of fasting. The subjects were then given 75 gm of glucose. The ECG was again recorded after postprandial 1 h (PP1H) and postprandial 2 h (PP2H) intervals. The linear and nonlinear HRV indices were calculated. The correlation coefficient was obtained by Spearman’s correlation test and regression analysis. The strong correlation was verified for SD1 and SD2 with linear HRV measures; however, the degree of correlation was changing during fasting and postprandial states. SD1/SD2 was strongly correlated with LF/HF ratio, Detrended Fluctuation Analysis (DFA) (α1, α2), suggesting nonlinear characteristics of the parameters. The entropy measures were not statistically significant with any index of Poincare plot. It can be concluded that the correlations exist between linear and nonlinear HRV measures and degree of correlation changes during fasting and postprandial states. Therefore, in studies correlating linear and nonlinear HRV, the fasting and postprandial states must be specifically identified.
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References
Mansier P, Clairambault J, Charlotte N, Medigue C, Vermeiren C, Lepape G et al (1996) Linear and nonlinear analyses of heart rate variability: a mini review. Cardiovasc Res 31:371–379
Frank A, Belokopytov M, Moran D, Shapiro Y, Epstein Y (2001) Changes in heart rate variability following acclimation to heart. J Basic Clin Physiol Pharmacol 12:19–32
Berntson GG, Bigger JT, Eckberg DL, Grossman P, Kaufmann PG, Malik M et al (1997) Heart rate variability: origin, methods and interpretive caveats. Psychophysiology 34:623–648
Nitzan M, Boer H, Turivnenko S, Babchenko A, Sapoznikov D (1994) Power spectrum analysis of spontaneous fluctuations in the photoplethysmographic signal. J Basic Clin Physiol Pharmacol 5:269–276
Malik M (1996) Task force of the European society of cardiology and the North American society of pacing and electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Eur Heart J 17:354–381
Acharya UR, Joseph KP, Kannathal N, Lim CM, Suri JS (2006) Heart rate variability: a review. Med Bio Eng Comput 44:1031–1051
Brennan M, Palaniswami M, Kamen P (2001) Do existing measures of poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Trans Biomed Eng 48:1342–1347
Mourot L, Bouhaddi M, Perry S, Rouillon JD, Regnard J (2004) Quantitative Poincare plot analysis of heart rate variability: effect of endurance training. Eur J Appl Physiol 91:79–87
Hoshi RA, Pastre CM, Marques Vanderlei LC, Godoy MF (2013) Poincare plot indexes of heart rate variability: relationship with other nonlinear variables. Auton Neurosci 177:271–274
Contreras P, Canetti R, Migliaro ER (2007) Correlations between frequency domain HRV indices and lagged Poincare plot width in healthy and diabetic subjects. Physiol Meas 28:85–94
Rowe JW, Young JB, Minaker KL, Stevens AL, Pallotta J, Landsberg L (1981) Effect of insulin and glucose infusion on sympathetic nerve system activity in normal man. Diabetes 30:219–225
Spraul M, Anderson EA, Bogardus C, Ravussin E (1994) Muscle sympathetic nerve activity in response to glucose ingestion. Diabetes 43:191–196
Rothberg LJ, Lees T, Bligh RC (2016) Association between heart rate variability measures and blood glucose levels: implications for noninvasive glucose monitoring for diabetes. Diabetes Technol Ther 18:366–374
Johncy SS, Karthik CS, Bondade SY, Jayalakshmi MK (2015) Altered cardiovascular autonomic function in young normotensive offspring of hypertensive parents—is obesity an additional risk factor. J Basic Clin Physiol Pharmacol 26:531–537
Sharma VK, Subramanian SK, Radhakrishnan K, Rajendran R, Ravindran BS, Arunachalam V (2017) Comparison of structured and unstructured physical activity training on predicted VO2max and heart rate variability in adolescents—a randomized control trial. J Basic Clin Physiol Pharmacol 28:225–238
Karmakar CK, Khandoker AH, Gubbi J, Palaniswami M (2009) Complex correlation measure: a novel descriptor for Poincare plot. Biomed Eng Online 8:17
Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci 88:2297–2301
Lewis MJ, Short AL (2007) Sample entropy of electrocardiographic RR and QT time series data during rest and exercise. Physiol Meas 28:731–744
Acharya UR, Lim CM, Joseph P (2002) Heart rate variability analysis using correlation dimension and detrended fluctuation analysis. ITBM-RBM 23:333–339
Mukaka MM (2012) Statistics corner: a guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24:69–71
Otzenberger H, Gronfier C, Simon C, Charloux A, Ehrhart J, Piquard F et al (1998) Dynamic heart rate variability: a tool for exploring sympathovagal balance continuously during sleep in men. Am J Physiol 275:H946–H950
Acharya UR, Kannathal N, Sing OW, Ping LY, Chua T (2004) Heart rate analysis in normal subjects of various age groups. Biomed Eng Online 3:24–31
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The authors are grateful to the volunteer participation of the students and staff of Government Engineering College Bikaner, Rajasthan. The ethical committee of the institution is also acknowledged for providing guidance and ethical clearance.
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Jain, J.K., Maheshwari, R. (2019). Correlation Between Poincare Plot Indices and Linear–Nonlinear Heart Rate Variability During Fasting and Postprandial States. In: Mishra, S., Sood, Y., Tomar, A. (eds) Applications of Computing, Automation and Wireless Systems in Electrical Engineering. Lecture Notes in Electrical Engineering, vol 553. Springer, Singapore. https://doi.org/10.1007/978-981-13-6772-4_77
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DOI: https://doi.org/10.1007/978-981-13-6772-4_77
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