Complex fluctuations within physiological signals can be used to evaluate the health of the human body. This study recruited four groups of subjects: young healthy subjects (Group 1,
Multiple temporal and spatial scales produce complex fluctuations within the output signals of physiological systems [
Many recent studies [
Despite the fact that Co_ApEn has been widely applied to evaluate the complexity between two time series [
This study evaluated the influences of age and diabetes on RRI and PTT. Considering that RRI and PTT are nonlinear, cardiovascular variables, we tested the applicability of MCE in the study subjects and investigated whether this dynamic parameter could provide further information related to the clinical control of diabetes.
Between July 2009 and March 2012, four groups of subjects were recruited for this study: young healthy subjects (Group 1, age range: 18–40,
All subjects were permitted to rest in a supine position in a quiet, temperature-controlled room at 25 ± 1°C for 5 minutes prior to subsequent 30-minute measurements. Again, a good reproducibility of six-channel ECG-PWV system [
1000 consecutive data points from ECG signals and PPG signals: PTT(
Using ECG and photoplethysmography (PPG), we obtained the RRI series
Due to a trend within physiological signals [
Previous studies [
MSE involves the use of a scale factor
Previous studies [ For given Define the distance between the vectors With the given let Average the logarithm of Increase Finally, take
The values of
Sample entropy (
Average values were expressed as mean ± SD. Significant differences in anthropometric, hemodynamic, and computational parameters (i.e., RRI, PTT, MCEISS, and MCEILS) between different groups were determined using an independent sample
Table
Comparisons of demographic, anthropometric, and serum biochemical parameters, MCEISS, and MCEILS among different subject populations.
Parameters | Group 1 | Group 2 | Group 3 | Group 4 |
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Age, year |
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Body height, cm |
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Body weight, kg |
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BMI, kg/m2 |
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Waist circumference, cm |
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SBP, mmHg |
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DBP, mmHg |
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PP, mmHg |
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HbA1c, % |
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Triglyceride, mg/dL |
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Fasting blood sugar, mg/dL |
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MEISS(RRI) |
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MEILS(RRI) |
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MEISS(PTT) |
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MEILS(PTT) |
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MCEISS |
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MCEILS |
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Group 1: healthy young subjects, Group 2: healthy upper middle-aged subjects, Group 3: type 2 diabetic well-controlled patients, Group 4: type 2 diabetic poorly controlled patients. Values are expressed as mean ± SD. BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; PP: pulse pressure; HbA1c: glycosylated hemoglobin; MEISS(RRI): R-R interval-based multiscale entropy index with small scale; MEILS(RRI): R-R interval-based multiscale entropy index with large scale; MEISS(PTT): pulse transit time-based multiscale entropy index with small scale; MEILS(PTT): pulse transit time-based multiscale entropy index with large scale; MCEISS: multiscale
There were no significant differences in the values of
Multiscale entropy (MSE) analysis of (a) RRI and (b) PTT time series showing changes in sample entropy,
Figure
Beginning at a scale factor of 3 (
MCEISS only presented a significant difference between Groups 1 and 2 (10.18 ± 0.52 versus 9.42 ± 0.70,
Since Pincus and Singer’s study [
Previous studies [
Drinnan et al.’s study [
Although the MCEILS can be used to quantify the complexity of RRI and PTT and have been shown to effectively identify significant difference among study groups, limitations still exist. First, a lengthy process of data acquisition and considerable calculation and off-line processing is needed. MCE analysis involves a 30-minute measurement, as opposed to the relatively shorter duration measurement of only RRI and PTT, making the process tiring for participants. The nature of analysis postmeasurement further prevented subjects from receiving their MCEI test results immediately. Second, the medications that the diabetic patients used such as hypoglycemic, antihyperlipidemic, and antihypertensive drugs may also affect autonomic nervous activity. These effects, however, were difficult to assess. The potential effect of medications, therefore, was not considered in the statistical analysis of this study.
This study integrates cross-approximate entropy with multiple scales to analyze the complexity between two synchronous physiological signals (RRI and PTT) in the cardiovascular system. According to our results, MCEILS clearly reveals a reduction in the complexity of two physiological signals caused by aging and diabetes.
M.-T. Lo and A.-B. Liu equally contributed in this study compared with the corresponding author.
The authors declare no conflict of interests.
The authors would like to thank the volunteers involved in this study for allowing them to collect and analyze their data. The authors are grateful for the support of Texas Instruments, Taiwan, in sponsoring the low power instrumentation amplifiers and ADC tools. The authors would also like to thank Miss Crystal J. McRae who is a native English speaker, to go over the whole paper. This research was partly supported by National Science Council under Grants NSC 100-2221-E-259-030-MY2 and NSC 101-2221-E-259-012 and National Dong Hwa University on campus interdisciplinary integration Projects no. 101T924-3 and 102T931-3. M.-T. Lo was supported by NSC (Taiwan, ROC), Grant no. 100-2221-E-008-008-MY2, joint foundation of CGH and NCU, Grant no. CNJRF-101CGH-NCU-A4, VGHUST102-G1-2-3 and NSC support for the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan (NSC 101-2911-I-008-001).