ArticleOpen Access

AGE-BASED ANALYSIS OF HEART RATE VARIABILITY (HRV) FOR PATIENTS WITH CONGESTIVE HEART FAILURE

College of Engineering and Science, Victoria University, Melbourne, Australia

Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czechia

E-mail Address: hamidreza.namazi@vu.edu.au

Corresponding author.

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DUMITRU BALEANU

Department of Mathematics, Cankaya University, Ankara, Turkey

Institute of Space Sciences, Magurele-Bucharest, Romania

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, and

ONDREJ KREJCAR

Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czechia

Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia

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https://doi.org/10.1142/S0218348X21501358Cited by:26 (Source: Crossref)

Abstract

It is known that heart activity changes during aging. In this paper, we evaluated alterations of heart activity from the complexity point of view. We analyzed the variations of heart rate of patients with congestive heart failure that are categorized into four different age groups, namely 30–39, 50–59, 60–69, and 70–79 years old. For this purpose, we employed three complexity measures that include fractal dimension, sample entropy, and approximate entropy. The results showed that the trend of increment of subjects’ age is reflected in the trend of increment of the complexity of heart rate variability (HRV) since the values of fractal dimension, approximate entropy, and sample entropy increase as subjects get older. The analysis of the complexity of other physiological signals can be further considered to investigate the variations of activity of other organs due to aging.

Keywords:

References

1. K. M. Keller and S. E. Howlett , Sex differences in the biology and pathology of the aging heart, Can. J. Cardiol. 32(9) (2016) 1065–1073. Web of Science, Google Scholar
2. M. Reardon and M. Malik , Changes in heart rate variability with age, Pacing Clin. Electrophysiol. 19(11 Pt 2) (1996) 1863–1866. Web of Science, Google Scholar
3. V. K. Jandackova, S. Scholes, A. Britton and A. Steptoe , Are changes in heart rate variability in middle-aged and older people normative or caused by pathological conditions? Findings from a large population-based longitudinal cohort study, J. Am. Heart Assoc. 5(2) (2016) e002365. Web of Science, Google Scholar
4. J. P. H. Tan, J. E. Beilharz, U. Vollmer-Conna and E. Cvejic , Heart rate variability as a marker of healthy ageing, Int. J. Cardiol. 275 (2019) 101–103. Web of Science, Google Scholar
5. M. A. Almeida-Santos et al., Aging, heart rate variability and patterns of autonomic regulation of the heart, Arch. Gerontol. Geriatr. 63 (2016) 1–8. Web of Science, Google Scholar
6. S. W. Niu et al., Association between age and changes in heart rate variability after hemodialysis in patients with diabetes, Front. Aging Neurosci. 10 (2018) 43. Web of Science, Google Scholar
7. National Clinical Guideline Centre (UK), Chronic Heart Failure: National Clinical Guideline for Diagnosis and Management in Primary and Secondary Care (Royal College of Physicians (UK), London, 2010). Google Scholar
8. A. Ahamed et al., Complexity based analysis of the influence of machining parameters on the surface finish of drilled holes in drilling operation, Fractals 27(6) (2019) 1950087. Link, Web of Science, Google Scholar
9. C. L. Kiew , Fractal-based analysis of the relation between the fractal structures of machined surface and tool wear in turning operation, Fractals 27(6) (2019) 1950094. Link, Web of Science, Google Scholar
10. M. Soundirarajan et al., Analysis of brain-facial muscle connection in the static fractal visual stimulation, Int. J. Imaging Syst. Technol. 1–7 (2020), https://doi.org/10.1002/ima.22480. Web of Science, Google Scholar
11. M. Soundirarajan, M. Augustynek, O. Krejcar and H. Namazi , Evaluation of the correlation between facial muscle and brain activities in auditory stimulation, Fractals 29(1) (2021) 2150100. Link, Web of Science, Google Scholar
12. H. Namazi and O. Krejcar , Analysis of pregnancy development by complexity and information-based analysis of fetal phonocardiogram (PCG) signals, Fluct. Noise Lett. (2020), https://doi.org/10.1142/S0219477521500280. Link, Web of Science, Google Scholar
13. R. H. Namazi , Fractal-based analysis of the influence of music on human respiration, Fractals 25(6) (2017) 1750059. Link, Web of Science, Google Scholar
14. H. Alipour, H. Namazi, H. Azarnoush and S. Jafari , Complexity-based analysis of the influence of visual stimulus color on human eye movement, Fractals 27(2) (2019) 1950002. Link, Web of Science, Google Scholar
15. S. Omam et al., Complexity-based decoding of brain-skin relation in response to olfactory stimuli, Comput. Methods Programs Biomed. 184 (2020) 105293, https://doi.org/10.1016/j.cmpb.2019.105293. Web of Science, Google Scholar
16. M. R. A. Ahamed, M. H. Babini and H. Namazi , Complexity-based decoding of the relation between human voice and brain activity, Technol. Health Care (2020), https://doi.org/10.3233/THC-192105. Web of Science, Google Scholar
17. H. Namazi , Complexity-based classification of the coronavirus genome versus genomes of the human immunodeficiency virus (HIV) and dengue virus, Fractals 28(7) (2020) 2050129. Link, Web of Science, Google Scholar
18. T. H. Mäkikallio et al., Prediction of sudden cardiac death by fractal analysis of heart rate variability in elderly subjects, J. Am. Coll. Cardiol. 37(5) (2001) 1395–1402. Web of Science, Google Scholar
19. Y. Yamamoto et al., On the fractal nature of heart rate variability in humans: Effects of vagal blockade, Am. J. Physiol. 269(4 Pt 2) (1995) R830–R837. Google Scholar
20. V. Singh, A. Gupta, J. S. Sohal and A. Singh , Multi-scale fractal dimension to quantify heart rate variability and systolic blood pressure variability: A postural stress analysis, Fluct. Noise Lett. 18(4) (2019) 1950019. Link, Web of Science, Google Scholar
21. P. Castiglioni and G. Merati , Fractal analysis of heart rate variability reveals alterations of the integrative autonomic control of circulation in paraplegic individuals, Physiol. Meas. 38(5) (2017) 774–786. Web of Science, Google Scholar
22. K. Vikram et al., Fractal dimension and approximate entropy of heart period and heart rate: Awake versus sleep differences and methodological issues, Clin. Sci. 95 (1998) 295–301. Web of Science, Google Scholar
23. D. M. Garner, N. M. de Souza and L. C. M. Vanderlei , Heart rate variability analysis: Higuchi and Katz’s fractal dimensions in subjects with type 1 diabetes mellitus, Rom. J. Diabetes Nutr. Metab. Dis. 25(3) (2018) 289–295. Google Scholar
24. A. Kikuchi et al., Changes in fractal features of fetal heart rate during pregnancy, Early Hum. Dev. 81(8) (2005) 655–661. Web of Science, Google Scholar
25. S. M. Kamal et al., Complexity-based decoding of the coupling among Heart Rate Variability (HRV) and walking path, Front. Physiol. 11 (2020) 602027, https://doi.org/10.3389/fphys.2020.602027. Web of Science, Google Scholar
26. H. Namazi, T. Seifi Ala and E. Aghasian , Complexity-based classification of EEG signal in normal subjects and patients with epilepsy, Technol. Health Care 28(1) (2020) 57–66. Web of Science, Google Scholar
27. S. A. Ahmad and P. H. Chappell , Surface EMG classification using moving approximate entropy, 2007 Int. Conf. Intelligent and Advanced Systems, Kuala Lumpur (2007), pp. 1163–1167, https://doi.org/10.1109/ICIAS.2007.4658567. Google Scholar
28. C. Gómez and R. Hornero , Entropy and complexity analyses in Alzheimer’s disease: An MEG study, Open Biomed. Eng. J. 4 (2010) 223–235. Web of Science, Google Scholar
29. R. A. Metzger, J. F. Doherty and D. M. Jenkins , Using approximate entropy as a speech quality measure for a speaker recognition system, 2016 Annual Conf. Information Science and Systems (CISS), Princeton, NJ (2016), pp. 292–297, https://doi.org/10.1109/CISS.2016.7460517. Google Scholar
30. H. Tamaki et al., Approximate entropy of respiratory movements in human newborns during different sleep states, Yonago Acta Med. 59(1) (2016) 89–91. Web of Science, Google Scholar
31. B. Shi et al., Entropy analysis of short-term heartbeat interval time series during regular walking, Entropy 19 (2017) 568. Web of Science, Google Scholar
32. S. Byun et al., Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study, Technol. Health Care. 27(Suppl 1) (2019) 407–424. Web of Science, Google Scholar
33. F. Beckers, D. Ramaekers and A. E. Aubert , Approximate entropy of heart rate variability: Validation of methods and application in heart failure, Cardiovasc. Eng. 1 (2001) 177–182, https://doi.org/10.1023/A:1015212328405. Google Scholar
34. M. K. Yum, J. H. Hwang and M. I. Park , Development of approximate entropy of fetal heart rate variability during normal pregnancy, J. Korean Pediatr. Soc. 40(4) (1997) 464–472. Google Scholar
35. G. Valenza, P. Allegrini, A. Lanatà and E. P. Scilingo , Dominant Lyapunov exponent and approximate entropy in heart rate variability during emotional visual elicitation, Front. Neuroeng. 5(3) (2012), https://doi.org/10.3389/fneng.2012.00003. Google Scholar
36. V. K. Yeragani et al., Fractal dimension and approximate entropy of heart period and heart rate: Awake versus sleep differences and methodological issues, Clin. Sci. (London) 95(3) (1998) 295–301. Web of Science, Google Scholar
37. L. Xia et al., Cardiovascular autonomic function analysis using approximate entropy from 24-h heart rate variability and its frequency components in patients with type 2 diabetes, J. Diabetes Investig. 6(2) (2015) 227–235. Web of Science, Google Scholar
38. E. N. Bruce, M. C. Bruce and S. Vennelaganti , Sample entropy tracks changes in EEG power spectrum with sleep state and aging, J. Clin. Neurophysiol. 26(4) (2009) 257–266. Web of Science, Google Scholar
39. Y. Chen et al., Stroke-related changes in the complexity of muscle activation during obstacle crossing using fuzzy approximate entropy analysis, Front. Neurol. 9(131) (2018), https://doi.org/10.3389/fneur.2018.00131. Google Scholar
40. G. Sun et al., The complexity analysis of voiced and unvoiced speech signal based on sample entropy, 2017 Fourth Int. Conf. Mathematics and Computers in Sciences and in Industry (MCSI), Corfu (2017), pp. 26–29, https://doi.org/10.1109/MCSI.2017.14. Google Scholar
41. M. J. Brookes et al., Complexity measures in magnetoencephalography: Measuring “disorder” in Schizophrenia, PLoS One 10(4) (2015) e0120991. Web of Science, Google Scholar
42. H. M. Al-Angari and A. V. Sahakian , Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome, IEEE Trans. Biomed. Eng. 54(10) (2007) 1900–1904. Web of Science, Google Scholar
43. M. J. Lewis and A. L. Short , Sample entropy of electrocardiographic RR and QT time-series data during rest and exercise, Physiol. Meas. 28(6) (2007) 731–744. Web of Science, Google Scholar
44. A. V. Bakhchina et al., Sample entropy of the heart rate reflects properties of the system organization of behavior, Entropy 20 (2018) 449. Web of Science, Google Scholar
45. B. Singh, D. Singh, A. K. Jaryal and K. K. Deepak , Ectopic beats in approximate entropy and sample entropy-based HRV assessment, Int. J. Syst. Sci. 43(5) (2012) 884–893. Web of Science, Google Scholar
46. J. F. Valencia et al., Multiscale sample entropy in heart rate variability of aortic stenosis patients, in Proc. 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS’08 — “Personalized Healthcare Through Technology” (2008), pp. 2000–2003. Google Scholar
47. D. E. Lake, J. Richman, M. P. Griffin and J. R. Moorman , Sample entropy analysis of neonatal heart rate variability, Am. J. Physiol. Regul. Integr. Comp. Physiol. 283(3) (2002) R789–R797. Web of Science, Google Scholar
48. M. O. Qadri and H. Namazi , Fractal-based analysis of the relation between tool wear and machine vibration in milling operation, Fractals 28(6) (2020) 2050101. Link, Web of Science, Google Scholar
49. H. Namazi, E. Herrera-Viedma and O. Krejcar , Complexity-based detection of similarity between animal coronaviruses and SARS-COV-2 in humans, Fractals 28(7) (2020) 2150031. Link, Web of Science, Google Scholar
50. A. L. Goldberger et al., PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals, Circulation 101(23) (2003) e215–e220. Google Scholar
51. https://doi.org/10.13026/C2F598. Google Scholar
52. H. Krum, J. T. Jr. Bigger, R. L. Goldsmith and M. Packer , Effect of long-term digoxin therapy on autonomic function in patients with chronic heart failure, J. Am. Coll. Cardiol. 25(2) (1995) 289–294. Web of Science, Google Scholar
53. A. C. Takahashi et al., Aging reduces complexity of heart rate variability assessed by conditional entropy and symbolic analysis, Int. Emerg. Med. 7(3) (2012) 229–235. Web of Science, Google Scholar
54. D. T. Kaplan , Aging and the complexity of cardiovascular dynamics, Biophys. J. 59(4) (1991) 945–949. Web of Science, Google Scholar
55. W. Mumtaz, L. Xia, A. S. Malik and M. A. M. Yasin , Complexity analysis of EEG data during rest state and visual stimulus, in Neural Information Processing. ICONIP 2012, eds. T. Huang, Z. Zeng, C. Li and C. S. Leung , Lecture Notes in Computer Science, Vol. 7663 (Springer, Berlin, 2012). Google Scholar
56. H. Namazi et al., Analysis of the influence of memory content of auditory stimuli on the memory content of EEG signal, Oncotarget 7(35) (2016) 56120–56128. Google Scholar
57. J. M. Alipour, R. Khosrowabadi and H. Namazi , Fractal-based analysis of the influence of variations of rhythmic patterns of music on human brain response, Fractals 26(5) (2018) 1850080. Link, Web of Science, Google Scholar
58. H. Namazi and V. V. Kulish , Complexity-based classification of the coronavirus disease (COVID-19), Fractals 28(5) (2020) 2050114. Link, Web of Science, Google Scholar
59. H. Namazi and V. V. Kulish , Fractional diffusion based modeling and prediction of human brain response to external stimuli, Comput. Math. Methods Med. 2015 (2015) 148534, https://doi.org/10.1155/2015/148534. Web of Science, Google Scholar
60. F. Pourpanah, R. Wang, C. P. Lim and D. Yazdani, A review of the family of artificial fish swarm algorithms: Recent advances and applications, preprint (2020), arXiv:2011.05700. Google Scholar
61. F. Pourpanah et al., A review of generalized zero-shot learning methods, preprint (2020), arXiv:2011.08641 (2020). Google Scholar

Vol. 29, No. 03

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Received 4 December 2020

Accepted 6 March 2021

Published: March 30, 2021

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This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND) License which permits use, distribution and reproduction, provided that the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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AGE-BASED ANALYSIS OF HEART RATE VARIABILITY (HRV) FOR PATIENTS WITH CONGESTIVE HEART FAILURE

Abstract

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