Heart rate variability series analyzing by fuzzy logic approach
Introdution. Exercise can be defined as any structured and planned activity leading to an increase of energy expenditure, breathing and pulse rate. In the context of a correct lifestyle, a regular physical activity reduces the probability of cardiovascular events, diabetes and other possible related...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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V. N. Karazin Kharkiv National University
2021-12-01
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| Series: | Journal of V. N. Karazin Kharkiv National University: Series Medicine |
| Subjects: | |
| Online Access: | https://periodicals.karazin.ua/medicine/article/view/18013 |
| _version_ | 1811191205012176896 |
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| author | Gianfranco Raimondi Alexander Martynenko L. Barsi Liudmila Maliarova |
| author_facet | Gianfranco Raimondi Alexander Martynenko L. Barsi Liudmila Maliarova |
| author_sort | Gianfranco Raimondi |
| collection | DOAJ |
| description | Introdution. Exercise can be defined as any structured and planned activity leading to an increase of energy expenditure, breathing and pulse rate. In the context of a correct lifestyle, a regular physical activity reduces the probability of cardiovascular events, diabetes and other possible related diseases. The aim of this study is to evaluate the neurovegetative cardiovascular regulation and the fluids distribution in healthy subjects undergoing dynamic and isometric training regimes. We have employed Heart Rate Variability (HRV) analysis by various mathematical methods that are classified as Time Domain (TD), Frequency Domain (FD) and Nonlinear (NM). We incorporated currently existing HRV indicators into a unified Fuzzy Logic (FL) methodology, which in turn will allow to integrally assessing each metric and HRV results as a whole. Objective. The goal of this study is to verify the response of the ANS before and after the execution of different training in the clearest view by our Fuzzy Logic approach to Heart Rate Variability series analysing. Our Fuzzy Logic algorithm incorporate into a single view of each metric, – Time Domain, Frequency Domain, Nonlinear Methods and HRV as a whole. Materials and methods. 24 young subjects aged between 20 and 30 (11 males and 13 females) have been enrolled. Exclusion criteria are: tobacco use; BMI > 25 kg/m2; cardiovascular diseases; blood pressure ≥ 140/90 mmHg; chronic pathologies; sport competition. Each of the examined subjects underwent four different tests and analyses: before the beginning of the isotonic training, which has been carried out by 30-minute run each day for a period of 20 days, and after the end of the training, both in upright and supine position; before the beginning of the isometric training, which has been carried out by lifting a 2-kg weight for 30 minutes per day for a period of 20 days, and after the end of the training, both in upright and supine position. Conclusion. HRV is a complex phenomenon, study of which requires various approaches and methods. However, a comprehensive view of HRV is only possible when there is a technology similar to Fuzzy Logic, one that allows combining all used methods and approaches into an integral assessment. In this article, we showed the Fuzzy Logic approach for series of Heart Rate Variability records and we can assert that: the training through exercises of dynamic type could reduce the cardiovascular risk, thus confirming the importance of a correct lifestyle; the isometric exercise generally produces an increase of the indexes of the sympathetic activity and then an increase of the cardiovascular risk with reduced cardioprotection; the Base state (before training) showing the biggest distance from abnormality because the Norm HRV values were defined for calm body state – before any training or disturbances; FL distances after Isometric training showing the worst distance from abnormality. |
| first_indexed | 2024-04-11T15:01:29Z |
| format | Article |
| id | doaj.art-3bad59fe5cdb411d8d44d73a081b23f3 |
| institution | Directory Open Access Journal |
| issn | 2313-6693 2313-2396 |
| language | English |
| last_indexed | 2024-04-11T15:01:29Z |
| publishDate | 2021-12-01 |
| publisher | V. N. Karazin Kharkiv National University |
| record_format | Article |
| series | Journal of V. N. Karazin Kharkiv National University: Series Medicine |
| spelling | doaj.art-3bad59fe5cdb411d8d44d73a081b23f32022-12-22T04:16:56ZengV. N. Karazin Kharkiv National UniversityJournal of V. N. Karazin Kharkiv National University: Series Medicine2313-66932313-23962021-12-0143512https://doi.org/10.26565/2313-6693-2021-43-01Heart rate variability series analyzing by fuzzy logic approachGianfranco Raimondi0Alexander Martynenko1https://orcid.org/0000-0002-0609-2220L. Barsi2Liudmila Maliarova3Sapienza University of RomeV. N. Karazin Kharkiv National UniversitySapienza University of Rome (Italy)V. N. Karazin Kharkiv National University School of MedicineIntrodution. Exercise can be defined as any structured and planned activity leading to an increase of energy expenditure, breathing and pulse rate. In the context of a correct lifestyle, a regular physical activity reduces the probability of cardiovascular events, diabetes and other possible related diseases. The aim of this study is to evaluate the neurovegetative cardiovascular regulation and the fluids distribution in healthy subjects undergoing dynamic and isometric training regimes. We have employed Heart Rate Variability (HRV) analysis by various mathematical methods that are classified as Time Domain (TD), Frequency Domain (FD) and Nonlinear (NM). We incorporated currently existing HRV indicators into a unified Fuzzy Logic (FL) methodology, which in turn will allow to integrally assessing each metric and HRV results as a whole. Objective. The goal of this study is to verify the response of the ANS before and after the execution of different training in the clearest view by our Fuzzy Logic approach to Heart Rate Variability series analysing. Our Fuzzy Logic algorithm incorporate into a single view of each metric, – Time Domain, Frequency Domain, Nonlinear Methods and HRV as a whole. Materials and methods. 24 young subjects aged between 20 and 30 (11 males and 13 females) have been enrolled. Exclusion criteria are: tobacco use; BMI > 25 kg/m2; cardiovascular diseases; blood pressure ≥ 140/90 mmHg; chronic pathologies; sport competition. Each of the examined subjects underwent four different tests and analyses: before the beginning of the isotonic training, which has been carried out by 30-minute run each day for a period of 20 days, and after the end of the training, both in upright and supine position; before the beginning of the isometric training, which has been carried out by lifting a 2-kg weight for 30 minutes per day for a period of 20 days, and after the end of the training, both in upright and supine position. Conclusion. HRV is a complex phenomenon, study of which requires various approaches and methods. However, a comprehensive view of HRV is only possible when there is a technology similar to Fuzzy Logic, one that allows combining all used methods and approaches into an integral assessment. In this article, we showed the Fuzzy Logic approach for series of Heart Rate Variability records and we can assert that: the training through exercises of dynamic type could reduce the cardiovascular risk, thus confirming the importance of a correct lifestyle; the isometric exercise generally produces an increase of the indexes of the sympathetic activity and then an increase of the cardiovascular risk with reduced cardioprotection; the Base state (before training) showing the biggest distance from abnormality because the Norm HRV values were defined for calm body state – before any training or disturbances; FL distances after Isometric training showing the worst distance from abnormality.https://periodicals.karazin.ua/medicine/article/view/18013heart rate variabilityfuzzy logic |
| spellingShingle | Gianfranco Raimondi Alexander Martynenko L. Barsi Liudmila Maliarova Heart rate variability series analyzing by fuzzy logic approach Journal of V. N. Karazin Kharkiv National University: Series Medicine heart rate variability fuzzy logic |
| title | Heart rate variability series analyzing by fuzzy logic approach |
| title_full | Heart rate variability series analyzing by fuzzy logic approach |
| title_fullStr | Heart rate variability series analyzing by fuzzy logic approach |
| title_full_unstemmed | Heart rate variability series analyzing by fuzzy logic approach |
| title_short | Heart rate variability series analyzing by fuzzy logic approach |
| title_sort | heart rate variability series analyzing by fuzzy logic approach |
| topic | heart rate variability fuzzy logic |
| url | https://periodicals.karazin.ua/medicine/article/view/18013 |
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