Abstract
The RR-interval time series or tachograms obtained from electrocardiograms have been widely studied since they reflect the cardiac variability, and this is an indicative of the health status of a person. The tachogram can be seen as a highly non-linear and complex time series, and therefore, should be analyzed with non-linear techniques. In this work, several entropy measures, Sample Entropy (SampEn), Approximate Entropy (ApEn), and Fuzzy Entropy (FuzzyEn) are used as a measure of heart rate variability (HRV). Tachograms belonging to thirty-nine subjects were obtained from a cardiac stress test consisting of a rest period followed by a period of moderate physical activity. Subjects are grouped according to their physical activity using the IPAQ sedentary and active questionnaire, we work with youth and middle-aged adults. The entropy measures for each group show that for the sedentary subjects the values are high at rest and decrease appreciably with moderate physical activity, This happens for both young and middle-aged adults. These results are highly reproducible. In the case of the subjects that exercise regularly, an increase in entropy is observed or they tend to retain the entropy value that they had at rest. It seems that there is a possible correlation between the physical condition of a person with the increase or decrease in entropy during moderate physical activity with respect to the entropy at rest. It was also observed that entropy during longer physical activity tests tends to decrease as fatigue accumulates, but this decrease is small compared to the change that occurs when going from rest to physical activity.
Highlights
The variability of human heart rate (HRV), is obtained by measuring the beat-to-beat changes in the duration of the RR interval of the electrocardiogram (ECG), is the result of the combination of different physiological control systems, which operate on different scales temporary and that allow the functioning of the body to adapt to physical, environmental or other changes
Since taking the tolerance equal to 0.2 σx gives the intermediate values for all entropies, in this work we chose to evaluate the entropies with this tolerance value. In this manuscript we work with heart rate variability (HRV) series at rest and HRV series when exercising, we report the observed changes in entropy, that is, we evaluate Sample Entropy (SampEn), Approximate Entropy (ApEn), and Fuzzy Entropy (FuzzyEn) and we calculate the exercise-rest variations and report such changes, obviously the changes do not have the same values, but the trends are the same, that is, if we notice that there is a decrease in entropy, such decrease is observed in the three entropies, the average changes do not have the same value
We measured the entropy at rest and only for healthy subjects who do physical activity regularly a light increase in the entropy values was observed during the physical activity test, and a decrease in the entropy values was observed in those subjects with a sedentary lifestyle, this happens for middle-aged adults
Summary
The variability of human heart rate (HRV), is obtained by measuring the beat-to-beat changes in the duration of the RR interval of the electrocardiogram (ECG), is the result of the combination of different physiological control systems, which operate on different scales temporary and that allow the functioning of the body to adapt to physical, environmental or other changes. Such fluctuations have been represented as a superimposition of rhythms, which contribute to the neuroautonomic modulation of the heart rhythm in healthy conditions, and are altered by a wide variety of disease states. There is a greater use of ambulatory HRV measurement, usually with the use of a long-term ambulatory meter or Holter
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