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Multiscale cross-approximate entropy analysis as a measurement of complexity between ECG R-R interval and PPG pulse amplitude series among the normal and diabetic subjects.

Wu HT, Lee CY, Liu CC, Liu AB - Comput Math Methods Med (2013)

Bottom Line: There are significant differences of heart rate variability, LHR, between Groups 1 and 2 (1.94 ± 1.21 versus 1.32 ± 1.00, P = 0.031).In conclusion, this study employed the MC-ApEn method, integrating multiple temporal and spatial scales, to quantify the complex interaction between the two physical signals.The MC-ApEn(LS) parameter could accurately reflect disease process in diabetics and might be another way for assessing the autonomic nerve function.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan.

ABSTRACT
Physiological signals often show complex fluctuation (CF) under the dual influence of temporal and spatial scales, and CF can be used to assess the health of physiologic systems in the human body. This study applied multiscale cross-approximate entropy (MC-ApEn) to quantify the complex fluctuation between R-R intervals series and photoplethysmography amplitude series. All subjects were then divided into the following two groups: healthy upper middle-aged subjects (Group 1, age range: 41-80 years, n = 27) and upper middle-aged subjects with type 2 diabetes (Group 2, age range: 41-80 years, n = 24). There are significant differences of heart rate variability, LHR, between Groups 1 and 2 (1.94 ± 1.21 versus 1.32 ± 1.00, P = 0.031). Results demonstrated differences in sum of large scale MC-ApEn (MC-ApEn(LS)) (5.32 ± 0.50 versus 4.74 ± 0.78, P = 0.003). This parameter has a good agreement with pulse-pulse interval and pulse amplitude ratio (PAR), a simplified assessment for baroreflex activity. In conclusion, this study employed the MC-ApEn method, integrating multiple temporal and spatial scales, to quantify the complex interaction between the two physical signals. The MC-ApEn(LS) parameter could accurately reflect disease process in diabetics and might be another way for assessing the autonomic nerve function.

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Related in: MedlinePlus

1000 consecutive data points from ECG signals and PPG signals.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig1: 1000 consecutive data points from ECG signals and PPG signals.

Mentions: For the PPG signals, the potential difference between the peak and the valley, which was prior to the peak, was defined as the pulse amplitude of PPG signals. The time difference between the two continous peaks of ECG R wave was defined as RRI(i), and the amplitude difference of each PPG pulse wave was defined as PPGA(j), as shown in Figure 1. The data length of the series in this study was set at n = 1000.


Multiscale cross-approximate entropy analysis as a measurement of complexity between ECG R-R interval and PPG pulse amplitude series among the normal and diabetic subjects.

Wu HT, Lee CY, Liu CC, Liu AB - Comput Math Methods Med (2013)

1000 consecutive data points from ECG signals and PPG signals.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3794634&req=5

fig1: 1000 consecutive data points from ECG signals and PPG signals.
Mentions: For the PPG signals, the potential difference between the peak and the valley, which was prior to the peak, was defined as the pulse amplitude of PPG signals. The time difference between the two continous peaks of ECG R wave was defined as RRI(i), and the amplitude difference of each PPG pulse wave was defined as PPGA(j), as shown in Figure 1. The data length of the series in this study was set at n = 1000.

Bottom Line: There are significant differences of heart rate variability, LHR, between Groups 1 and 2 (1.94 ± 1.21 versus 1.32 ± 1.00, P = 0.031).In conclusion, this study employed the MC-ApEn method, integrating multiple temporal and spatial scales, to quantify the complex interaction between the two physical signals.The MC-ApEn(LS) parameter could accurately reflect disease process in diabetics and might be another way for assessing the autonomic nerve function.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan.

ABSTRACT
Physiological signals often show complex fluctuation (CF) under the dual influence of temporal and spatial scales, and CF can be used to assess the health of physiologic systems in the human body. This study applied multiscale cross-approximate entropy (MC-ApEn) to quantify the complex fluctuation between R-R intervals series and photoplethysmography amplitude series. All subjects were then divided into the following two groups: healthy upper middle-aged subjects (Group 1, age range: 41-80 years, n = 27) and upper middle-aged subjects with type 2 diabetes (Group 2, age range: 41-80 years, n = 24). There are significant differences of heart rate variability, LHR, between Groups 1 and 2 (1.94 ± 1.21 versus 1.32 ± 1.00, P = 0.031). Results demonstrated differences in sum of large scale MC-ApEn (MC-ApEn(LS)) (5.32 ± 0.50 versus 4.74 ± 0.78, P = 0.003). This parameter has a good agreement with pulse-pulse interval and pulse amplitude ratio (PAR), a simplified assessment for baroreflex activity. In conclusion, this study employed the MC-ApEn method, integrating multiple temporal and spatial scales, to quantify the complex interaction between the two physical signals. The MC-ApEn(LS) parameter could accurately reflect disease process in diabetics and might be another way for assessing the autonomic nerve function.

Show MeSH
Related in: MedlinePlus