Combination of R-R Interval and Crest Time in Assessing Complexity Using Multiscale Cross-Approximate Entropy in Normal and Diabetic Subjects
The present study aimed at testing the hypothesis that application of multiscale cross-approximate entropy (MCAE) analysis in the study of nonlinear coupling behavior of two synchronized time series of different natures [i.e., R-R interval (RRI) and crest time (CT, the time interval from foot to pea...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-06-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1099-4300/20/7/497 |
| _version_ | 1798002479404679168 |
|---|---|
| author | Ming-Xia Xiao Hai-Cheng Wei Ya-Jie Xu Hsien-Tsai Wu Cheuk-Kwan Sun |
| author_facet | Ming-Xia Xiao Hai-Cheng Wei Ya-Jie Xu Hsien-Tsai Wu Cheuk-Kwan Sun |
| author_sort | Ming-Xia Xiao |
| collection | DOAJ |
| description | The present study aimed at testing the hypothesis that application of multiscale cross-approximate entropy (MCAE) analysis in the study of nonlinear coupling behavior of two synchronized time series of different natures [i.e., R-R interval (RRI) and crest time (CT, the time interval from foot to peakof a pulse wave)] could yield information on complexity related to diabetes-associated vascular changes. Signals of a single waveform parameter (i.e., CT) from photoplethysmography and RRI from electrocardiogram were simultaneously acquired within a period of one thousand cardiac cycles for the computation of different multiscale entropy indices from healthy young adults (n = 22) (Group 1), upper-middle aged non-diabetic subjects (n = 34) (Group 2) and diabetic patients (n = 34) (Group 3). The demographic (i.e., age), anthropometric (i.e., body height, body weight, waist circumference, body-mass index), hemodynamic (i.e., systolic and diastolic blood pressures), and serum biochemical (i.e., high- and low-density lipoprotein cholesterol, total cholesterol, and triglyceride) parameters were compared with different multiscale entropy indices including small- and large-scale multiscale entropy indices for CT and RRI [MEISS(CT), MEILS(CT), MEISS(RRI), MEILS(RRI), respectively] as well as small- and large-scale multiscale cross-approximate entropy indices [MCEISS, MCEILS, respectively]. The results demonstrated that both MEILS(RRI) and MCEILS significantly differentiated between Group 2 and Group 3 (all p < 0.017). Multivariate linear regression analysis showed significant associations of MEILS(RRI) and MCEILS(RRI,CT) with age and glycated hemoglobin level (all p < 0.017). The findings highlight the successful application of a novel multiscale cross-approximate entropy index in non-invasively identifying diabetes-associated subtle changes in vascular functional integrity, which is of clinical importance in preventive medicine. |
| first_indexed | 2024-04-11T11:52:50Z |
| format | Article |
| id | doaj.art-32770026b94f48e5b895ce68452b3dd1 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T11:52:50Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-32770026b94f48e5b895ce68452b3dd12022-12-22T04:25:15ZengMDPI AGEntropy1099-43002018-06-0120749710.3390/e20070497e20070497Combination of R-R Interval and Crest Time in Assessing Complexity Using Multiscale Cross-Approximate Entropy in Normal and Diabetic SubjectsMing-Xia Xiao0Hai-Cheng Wei1Ya-Jie Xu2Hsien-Tsai Wu3Cheuk-Kwan Sun4School of Electrical and Information Engineering, North Minzu University, No. 204 North—Wenchang St., Xixia District, Yinchuan 750021, ChinaSchool of Electrical and Information Engineering, North Minzu University, No. 204 North—Wenchang St., Xixia District, Yinchuan 750021, ChinaSchool of Electrical and Information Engineering, North Minzu University, No. 204 North—Wenchang St., Xixia District, Yinchuan 750021, ChinaDepartment of Electrical Engineering, National Dong Hwa University, No. 1, Sec. 2, Da Hsueh Rd., Shoufeng, Hualien 97401, TaiwanDepartment of Emergency Medicine, E-Da Hospital, I-Shou University School of Medicine for International Students, No.1, Yida Road, Jiaosu Village, Yanchao District, Kaohsiung City 82445, TaiwanThe present study aimed at testing the hypothesis that application of multiscale cross-approximate entropy (MCAE) analysis in the study of nonlinear coupling behavior of two synchronized time series of different natures [i.e., R-R interval (RRI) and crest time (CT, the time interval from foot to peakof a pulse wave)] could yield information on complexity related to diabetes-associated vascular changes. Signals of a single waveform parameter (i.e., CT) from photoplethysmography and RRI from electrocardiogram were simultaneously acquired within a period of one thousand cardiac cycles for the computation of different multiscale entropy indices from healthy young adults (n = 22) (Group 1), upper-middle aged non-diabetic subjects (n = 34) (Group 2) and diabetic patients (n = 34) (Group 3). The demographic (i.e., age), anthropometric (i.e., body height, body weight, waist circumference, body-mass index), hemodynamic (i.e., systolic and diastolic blood pressures), and serum biochemical (i.e., high- and low-density lipoprotein cholesterol, total cholesterol, and triglyceride) parameters were compared with different multiscale entropy indices including small- and large-scale multiscale entropy indices for CT and RRI [MEISS(CT), MEILS(CT), MEISS(RRI), MEILS(RRI), respectively] as well as small- and large-scale multiscale cross-approximate entropy indices [MCEISS, MCEILS, respectively]. The results demonstrated that both MEILS(RRI) and MCEILS significantly differentiated between Group 2 and Group 3 (all p < 0.017). Multivariate linear regression analysis showed significant associations of MEILS(RRI) and MCEILS(RRI,CT) with age and glycated hemoglobin level (all p < 0.017). The findings highlight the successful application of a novel multiscale cross-approximate entropy index in non-invasively identifying diabetes-associated subtle changes in vascular functional integrity, which is of clinical importance in preventive medicine.http://www.mdpi.com/1099-4300/20/7/497multiscale entropy (MSE)cross-approximate entropycrest timeR-R intervaldiabetes |
| spellingShingle | Ming-Xia Xiao Hai-Cheng Wei Ya-Jie Xu Hsien-Tsai Wu Cheuk-Kwan Sun Combination of R-R Interval and Crest Time in Assessing Complexity Using Multiscale Cross-Approximate Entropy in Normal and Diabetic Subjects Entropy multiscale entropy (MSE) cross-approximate entropy crest time R-R interval diabetes |
| title | Combination of R-R Interval and Crest Time in Assessing Complexity Using Multiscale Cross-Approximate Entropy in Normal and Diabetic Subjects |
| title_full | Combination of R-R Interval and Crest Time in Assessing Complexity Using Multiscale Cross-Approximate Entropy in Normal and Diabetic Subjects |
| title_fullStr | Combination of R-R Interval and Crest Time in Assessing Complexity Using Multiscale Cross-Approximate Entropy in Normal and Diabetic Subjects |
| title_full_unstemmed | Combination of R-R Interval and Crest Time in Assessing Complexity Using Multiscale Cross-Approximate Entropy in Normal and Diabetic Subjects |
| title_short | Combination of R-R Interval and Crest Time in Assessing Complexity Using Multiscale Cross-Approximate Entropy in Normal and Diabetic Subjects |
| title_sort | combination of r r interval and crest time in assessing complexity using multiscale cross approximate entropy in normal and diabetic subjects |
| topic | multiscale entropy (MSE) cross-approximate entropy crest time R-R interval diabetes |
| url | http://www.mdpi.com/1099-4300/20/7/497 |
| work_keys_str_mv | AT mingxiaxiao combinationofrrintervalandcresttimeinassessingcomplexityusingmultiscalecrossapproximateentropyinnormalanddiabeticsubjects AT haichengwei combinationofrrintervalandcresttimeinassessingcomplexityusingmultiscalecrossapproximateentropyinnormalanddiabeticsubjects AT yajiexu combinationofrrintervalandcresttimeinassessingcomplexityusingmultiscalecrossapproximateentropyinnormalanddiabeticsubjects AT hsientsaiwu combinationofrrintervalandcresttimeinassessingcomplexityusingmultiscalecrossapproximateentropyinnormalanddiabeticsubjects AT cheukkwansun combinationofrrintervalandcresttimeinassessingcomplexityusingmultiscalecrossapproximateentropyinnormalanddiabeticsubjects |