Heart Rate Dynamics in Patients with Obstructive Sleep Apnea: Heart Rate Variability and Entropy
Background: Obstructive sleep apnea (OSA), a highly prevalent sleep disorder, is closely related to cardiovascular disease (CVD). Our previous work demonstrated that Shannon entropy of the degree distribution (E<sub>DD</sub>), obtained from the network domain of heart rate variability (H...
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| Format: | Article |
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MDPI AG
2019-09-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/10/927 |
| _version_ | 1817996469391589376 |
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| author | Lulu Zhang Mingyu Fu Fengguo Xu Fengzhen Hou Yan Ma |
| author_facet | Lulu Zhang Mingyu Fu Fengguo Xu Fengzhen Hou Yan Ma |
| author_sort | Lulu Zhang |
| collection | DOAJ |
| description | Background: Obstructive sleep apnea (OSA), a highly prevalent sleep disorder, is closely related to cardiovascular disease (CVD). Our previous work demonstrated that Shannon entropy of the degree distribution (E<sub>DD</sub>), obtained from the network domain of heart rate variability (HRV), might be a potential indicator for CVD. Method: To investigate the potential association between OSA and E<sub>DD</sub>, OSA patients and healthy controls (HCs) were identified from a sleep study database. Then E<sub>DD</sub> was calculated from electrocardiogram (ECG) signals during sleep, followed by cross-sectional comparisons between OSA patients and HCs, and longitudinal comparisons from baseline to follow-up visits. Furthermore, for OSA patients, the association between E<sub>DD</sub> and OSA severity, measured by apnea-hypopnea index (AHI), was also analyzed. Results: Compared with HCs, OSA patients had significantly increased E<sub>DD</sub> during sleep. A positive correlation between E<sub>DD</sub> and the severity of OSA was also observed. Although the value of E<sub>DD</sub> became larger with aging, it was not OSA-specified. Conclusion: Increased E<sub>DD</sub> derived from ECG signals during sleep might be a potential dynamic biomarker to identify OSA patients from HCs, which may be used in screening OSA with high risk before polysomnography is considered. |
| first_indexed | 2024-04-14T02:22:43Z |
| format | Article |
| id | doaj.art-0583b9188a2d44509bfb2712a174b22b |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-14T02:22:43Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-0583b9188a2d44509bfb2712a174b22b2022-12-22T02:17:59ZengMDPI AGEntropy1099-43002019-09-01211092710.3390/e21100927e21100927Heart Rate Dynamics in Patients with Obstructive Sleep Apnea: Heart Rate Variability and EntropyLulu Zhang0Mingyu Fu1Fengguo Xu2Fengzhen Hou3Yan Ma4Key Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing 210009, ChinaKey Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing 210009, ChinaKey Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Nanjing 210009, ChinaKey Laboratory of Biomedical Functional Materials, School of Science, China Pharmaceutical University, Nanjing 210009, ChinaCenter for Dynamical Biomarkers, Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USABackground: Obstructive sleep apnea (OSA), a highly prevalent sleep disorder, is closely related to cardiovascular disease (CVD). Our previous work demonstrated that Shannon entropy of the degree distribution (E<sub>DD</sub>), obtained from the network domain of heart rate variability (HRV), might be a potential indicator for CVD. Method: To investigate the potential association between OSA and E<sub>DD</sub>, OSA patients and healthy controls (HCs) were identified from a sleep study database. Then E<sub>DD</sub> was calculated from electrocardiogram (ECG) signals during sleep, followed by cross-sectional comparisons between OSA patients and HCs, and longitudinal comparisons from baseline to follow-up visits. Furthermore, for OSA patients, the association between E<sub>DD</sub> and OSA severity, measured by apnea-hypopnea index (AHI), was also analyzed. Results: Compared with HCs, OSA patients had significantly increased E<sub>DD</sub> during sleep. A positive correlation between E<sub>DD</sub> and the severity of OSA was also observed. Although the value of E<sub>DD</sub> became larger with aging, it was not OSA-specified. Conclusion: Increased E<sub>DD</sub> derived from ECG signals during sleep might be a potential dynamic biomarker to identify OSA patients from HCs, which may be used in screening OSA with high risk before polysomnography is considered.https://www.mdpi.com/1099-4300/21/10/927electrocardiogramheart rate dynamicsobstructive sleep apneagraph theoryentropy |
| spellingShingle | Lulu Zhang Mingyu Fu Fengguo Xu Fengzhen Hou Yan Ma Heart Rate Dynamics in Patients with Obstructive Sleep Apnea: Heart Rate Variability and Entropy Entropy electrocardiogram heart rate dynamics obstructive sleep apnea graph theory entropy |
| title | Heart Rate Dynamics in Patients with Obstructive Sleep Apnea: Heart Rate Variability and Entropy |
| title_full | Heart Rate Dynamics in Patients with Obstructive Sleep Apnea: Heart Rate Variability and Entropy |
| title_fullStr | Heart Rate Dynamics in Patients with Obstructive Sleep Apnea: Heart Rate Variability and Entropy |
| title_full_unstemmed | Heart Rate Dynamics in Patients with Obstructive Sleep Apnea: Heart Rate Variability and Entropy |
| title_short | Heart Rate Dynamics in Patients with Obstructive Sleep Apnea: Heart Rate Variability and Entropy |
| title_sort | heart rate dynamics in patients with obstructive sleep apnea heart rate variability and entropy |
| topic | electrocardiogram heart rate dynamics obstructive sleep apnea graph theory entropy |
| url | https://www.mdpi.com/1099-4300/21/10/927 |
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