COMPLEXITY-BASED ANALYSIS OF HEART RATE VARIABILITY DURING AGING

Norazryana Mat Dawi, Petra Maresova and Hamidreza Namazi
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Norazryana Mat Dawi: 525 West 8th Avenue, Vancouver, BC, Canada V5Z 1C6, Canada
Petra Maresova: ��Faculty of Informatics and Management, University of Hradec Kralove, Rokitanského, 62/26, 500 03 Hradec Kralove, Czechia‡Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya, Petra, Kuala Lumpur 54100, Malaysia
Hamidreza Namazi: ��Faculty of Informatics and Management, University of Hradec Kralove, Rokitanského, 62/26, 500 03 Hradec Kralove, Czechia§College of Engineering and Science, Victoria University, 70/104 Ballarat Rd, Footscray VIC 3011, Melbourne, Australia

FRACTALS (fractals), 2022, vol. 30, issue 09, 1-8

Abstract: One of the important areas of heart research is investigating how heart activity changes during aging. In this research, we employed complexity-based techniques to analyze how heart activity varies based on the age of subjects. For this purpose, the heart rate variability (HRV) of 54 healthy subjects (30 M, 24 F, 28.5–76 years old) in three different age groups was analyzed using fractal theory, sample entropy, and approximate entropy. We showed that the fractal dimension, sample entropy, and approximate entropy of the RR interval time series (as HRV) are related to the age of the subjects. In other words, as subjects get older, the complexity of their RR interval time series decreases. Therefore, we decoded the variations in HRV during aging. The method of analysis that was employed in this research can be used to analyze the variations of other physiological signals (e.g. Electroencephalogram (EEG) signals) during aging.

Keywords: Heart Rate Variability (HRV); Complexity; Aging; Fractal Theory; Sample Entropy; Approximate Entropy; RR Interval Time Series (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218348X22501985

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