Bivariate Entropy Analysis of Electrocardiographic RR–QT Time Series
QT interval variability (QTV) and heart rate variability (HRV) are both accepted biomarkers for cardiovascular events. QTV characterizes the variations in ventricular depolarization and repolarization. It is a predominant element of HRV. However, QTV is also believed to accept direct inputs from ups...
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MDPI AG
2020-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/12/1439 |
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| author | Bo Shi Mohammod Abdul Motin Xinpei Wang Chandan Karmakar Peng Li |
| author_facet | Bo Shi Mohammod Abdul Motin Xinpei Wang Chandan Karmakar Peng Li |
| author_sort | Bo Shi |
| collection | DOAJ |
| description | QT interval variability (QTV) and heart rate variability (HRV) are both accepted biomarkers for cardiovascular events. QTV characterizes the variations in ventricular depolarization and repolarization. It is a predominant element of HRV. However, QTV is also believed to accept direct inputs from upstream control system. How QTV varies along with HRV is yet to be elucidated. We studied the dynamic relationship of QTV and HRV during different physiological conditions from resting, to cycling, and to recovering. We applied several entropy-based measures to examine their bivariate relationships, including cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), cross conditional entropy (XCE), and joint distribution entropy (JDistEn). Results showed no statistically significant differences in XSampEn, XFuzzyEn, and XCE across different physiological states. Interestingly, JDistEn demonstrated significant decreases during cycling as compared with that during the resting state. Besides, JDistEn also showed a progressively recovering trend from cycling to the first 3 min during recovering, and further to the second 3 min during recovering. It appeared to be fully recovered to its level in the resting state during the second 3 min during the recovering phase. The results suggest that there is certain nonlinear temporal relationship between QTV and HRV, and that the JDistEn could help unravel this nuanced property. |
| first_indexed | 2024-03-10T13:54:18Z |
| format | Article |
| id | doaj.art-76fdb2bc2f8a4516a44cbb06a872f3b0 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T13:54:18Z |
| publishDate | 2020-12-01 |
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| series | Entropy |
| spelling | doaj.art-76fdb2bc2f8a4516a44cbb06a872f3b02023-11-21T01:45:21ZengMDPI AGEntropy1099-43002020-12-012212143910.3390/e22121439Bivariate Entropy Analysis of Electrocardiographic RR–QT Time SeriesBo Shi0Mohammod Abdul Motin1Xinpei Wang2Chandan Karmakar3Peng Li4School of Medical Imaging, Bengbu Medical College, Bengbu 233030, ChinaDepartment of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC 3110, AustraliaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaSchool of Information Technology, Deakin University, Geelong, VIC 3225, AustraliaDivision of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USAQT interval variability (QTV) and heart rate variability (HRV) are both accepted biomarkers for cardiovascular events. QTV characterizes the variations in ventricular depolarization and repolarization. It is a predominant element of HRV. However, QTV is also believed to accept direct inputs from upstream control system. How QTV varies along with HRV is yet to be elucidated. We studied the dynamic relationship of QTV and HRV during different physiological conditions from resting, to cycling, and to recovering. We applied several entropy-based measures to examine their bivariate relationships, including cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), cross conditional entropy (XCE), and joint distribution entropy (JDistEn). Results showed no statistically significant differences in XSampEn, XFuzzyEn, and XCE across different physiological states. Interestingly, JDistEn demonstrated significant decreases during cycling as compared with that during the resting state. Besides, JDistEn also showed a progressively recovering trend from cycling to the first 3 min during recovering, and further to the second 3 min during recovering. It appeared to be fully recovered to its level in the resting state during the second 3 min during the recovering phase. The results suggest that there is certain nonlinear temporal relationship between QTV and HRV, and that the JDistEn could help unravel this nuanced property.https://www.mdpi.com/1099-4300/22/12/1439cross entropyjoint distribution entropyRR–QT relationshipambulatory monitoring |
| spellingShingle | Bo Shi Mohammod Abdul Motin Xinpei Wang Chandan Karmakar Peng Li Bivariate Entropy Analysis of Electrocardiographic RR–QT Time Series Entropy cross entropy joint distribution entropy RR–QT relationship ambulatory monitoring |
| title | Bivariate Entropy Analysis of Electrocardiographic RR–QT Time Series |
| title_full | Bivariate Entropy Analysis of Electrocardiographic RR–QT Time Series |
| title_fullStr | Bivariate Entropy Analysis of Electrocardiographic RR–QT Time Series |
| title_full_unstemmed | Bivariate Entropy Analysis of Electrocardiographic RR–QT Time Series |
| title_short | Bivariate Entropy Analysis of Electrocardiographic RR–QT Time Series |
| title_sort | bivariate entropy analysis of electrocardiographic rr qt time series |
| topic | cross entropy joint distribution entropy RR–QT relationship ambulatory monitoring |
| url | https://www.mdpi.com/1099-4300/22/12/1439 |
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