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Cited by 29Heart Rate Variability and Autonomic ControlNon-Invasive Vital Sign MonitoringComplex Systems and Time Series Analysis

Multiscale Cross-Approximate Entropy Analysis as a Measure of Complexity among the Aged and Diabetic

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Wu, Hsien-Tsai, Liu, Cyuan-Cin, Lo, Men-Tzung, Hsu, Po-Chun, Liu, An-Bang, Chang, Kai-Yu, Tang, Chieh-Ju·2013·Computational and Mathematical Methods in Medicine
DOI 10.1155/2013/324325PMC PMC3705813
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Abstract

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,n=32), healthy upper middle-aged subjects (Group 2,n=36), subjects with well-controlled type 2 diabetes (Group 3,n=31), and subjects with poorly controlled type 2 diabetes (Group 4,n=24). Data acquisition for each participant lasted 30 minutes. We obtained data related to consecutive time series with R-R interval (RRI) and pulse transit time (PTT). Using multiscale cross-approximate entropy (MCE), we quantified the complexity between the two series and thereby differentiated the influence of age and diabetes on the complexity of physiological signals. This study used MCE in the quantification of complexity between RRI and PTT time series. We observed changes in the influences of age and disease on the coupling effects between the heart and blood vessels in the cardiovascular system, which reduced the complexity between RRI and PTT series.

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  • Prior works developed approaches from at most 100 individuals and in most cases recorded less than a day of data.

    “These works developed approaches from at most 100 individuals and in most cases, recorded less than a day of data.”

    From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [14]

    Methods §2.3 reports 30-minute measurements, supporting the claim that this study recorded less than a day of data. But Methods §2.2 lists four groups totaling 123 participants, so this study does not support the asserted maximum of 100 individuals. This paper alone cannot establish what happened in most of the other works. (LLM, conf=0.90)

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Research works printed but not linked: 24

  1. [1]Costa M Goldberger AL Peng CK Multiscale entropy analysis of biological signals Physical Review E 2005 71 2, part 1 15783351
  2. [2]Costa M Goldberger AL Peng CK Multiscale entropy to distinguish physiologic and synthetic RR time series Computing in Cardiology 2002 29 137 140 14686448
  3. [3]Wu H-T Hsu P-C Lin C-F Multiscale entropy analysis of pulse wave velocity for assessing atherosclerosis in the aged and diabetic IEEE Transactions on Biomedical Engineering 2011 58 10 2978 2981 2-s2.0-80053166674 21693413
  4. [4]Vieira RT Brunet N Costa SC Correia S Neto BGA Fechine JM Combining entropy measures and cepstral analysis for pathological voices assessment Journal of Medical and Biological Engineering 2012 32 6 429 435
  5. [5]Lan JY Abbod MF Yeh RG Fan SZ Shieh JS Review: intelligent modeling and control in anesthesia Journal of Medical and Biological Engineering 2012 32 5 293 307
  6. [6]Peng C-K Costa M Goldberger AL Adaptive data analysis of complex fluctuations in physiologic time series Advances in Adaptive Data Analysis 2009 1 1 61 70 2-s2.0-75949128348 20041035 PMC2798133
  7. [7]Drinnan MJ Allen J Murray A Relation between heart rate and pulse transit time during paced respiration Physiological Measurement 2001 22 3 425 432 2-s2.0-0034859994 11556663
  8. [9]Pincus SM Mulligan T Iranmanesh A Gheorghiu S Godschalk M Veldhuis JD Older males secrete luteinizing hormone and testosterone more irregularly, and jointly more asynchronously, than younger males Proceedings of the National Academy of Sciences of the United States of America 1996 93 24 14100 14105 2-s2.0-0030450771 8943067 PMC19501
  9. [10]Roelfsema F Pincus SM Veldhuis JD Patients with Cushing’s disease secrete adrenocorticotropin and cortisol jointly more asynchronously than healthy subjects Journal of Clinical Endocrinology and Metabolism 1998 83 2 688 692 2-s2.0-0031791252 9467594
  10. [11]Veldhuis JD Iranmanesh A Mulligan T Pincus SM Disruption of the young-adult synchrony between luteinizing hormone release and oscillations in follicle-stimulating hormone, prolactin, and nocturnal penile tumescence (NPT) in healthy older men Journal of Clinical Endocrinology and Metabolism 1999 84 10 3498 3505 2-s2.0-0033304566 10522986
  11. [12]Wu D-Y Cai G Yuan Y Application of nonlinear dynamics analysis in assessing unconsciousness: a preliminary study Clinical Neurophysiology 2011 122 3 490 498 2-s2.0-78651086477 20719560
  12. [13]Ahmed MU Mandic DP Multivariate multiscale entropy: a tool for complexity analysis of multichannel data Physical Review E 2011 84 6 2-s2.0-84855317596 061918 22304127
  13. [14]Liu A-B Hsu P-C Chen Z-L Wu H-T Measuring pulse wave velocity using ECG and photoplethysmography Journal of Medical Systems 2011 35 5 771 777 2-s2.0-84856210623 20703725
  14. [15]Wu HT Hsu PC Liu AB Chen ZL Huang RM Chen CP Six-channel ECG-based pulse wave velocity for assessing whole-body arterial stiffness Blood Press 2012 21 3 167 176 22519467
  15. [16]Wu Z Huang NE Long SR Peng C-K On the trend, detrending, and variability of nonlinear and nonstationary time series Proceedings of the National Academy of Sciences of the United States of America 2007 104 38 14889 14894 2-s2.0-35448991660 17846430 PMC1986583
  16. [17]Huang NE Shen Z Long SR The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis Proceedings of the Royal Society A 1998 454 1971 903 995 2-s2.0-5444236478
  17. [18]Costa MD Peng C-K Goldberger AL Multiscale analysis of heart rate dynamics: entropy and time irreversibility measures Cardiovascular Engineering 2008 8 2 88 93 2-s2.0-42649115762 18172763 PMC4801222
  18. [19]Pincus S Singer BH Randomness and degrees of irregularity Proceedings of the National Academy of Sciences of the United States of America 1996 93 5 2083 2088 2-s2.0-0029924587 11607637 PMC39913
  19. [20]Kreuzer M Hentschke H Antkowiak B Schwarz C Kochs EF Schneider G Cross-approximate entropy of cortical local field potentials quantifies effects of anesthesia—a pilot study in rats BMC Neuroscience 2010 11, article 122 2-s2.0-77956875982 PMC2955690 20863382
  20. [21]Pincus SM Irregularity and asynchrony in biologic network signals Methods in Enzymology 2000 321 149 182 2-s2.0-0033932574 10909056
  21. [22]Yang F Hong B Tang Q Akay M Approximate entropy and its application to biosignal analysis Nonlinear Biomedical Signal Processing: Dynamic Analysis and Modeling 2000 2 Hoboken, NJ, USA John Wiley & Sons
  22. [23]Cheng D Tsai S-J Hong C-J Yang AC Reduced physiological complexity in robust elderly adults with the APOE ε4 allele PLoS ONE 2009 4 11 2-s2.0-70450192899 e7733 PMC2767503 19890394
  23. [24]Vaillancourt DE Newell KM Changing complexity in human behavior and physiology through aging and disease Neurobiology of Aging 2002 23 1 1 11 2-s2.0-0036149367 11755010
  24. [25]Kaplan DT Furman MI Pincus SM Ryan SM Lipsitz LA Goldberger AL Aging and the complexity of cardiovascular dynamics Biophysical Journal 1991 59 4 945 949 2-s2.0-0025858949 2065195 PMC1281262

Cited by

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Authors
Wu, H.-T., Liu, C.-C., Lo, M.-T., Hsu, P.-C., Liu, A.-B., Chang, K.-Y., & Tang, C.-J.
Affiliations
  • National Dong Hwa University (TW)
  • National Central University (TW)
  • Tzu Chi University (TW)
  • Department of Internal Medicine, Hualien Hospital, Health Executive Yuan, Hualien 97061, Taiwan
Corresponding author
Wu, Hsien-Tsai
ORCID
Venue
Computational and Mathematical Methods in Medicine
Publisher
Wiley
Published
2013
Volume
2013
Pages
1-7
ISSN
  • 1748-670X (ISSN-L)
  • 1748-6718
DOI
10.1155/2013/324325
PMC
PMC3705813
OpenAlex
W2021816116
Cited by
29
References
0
Citations by year
2026: 1 · 2023: 2 · 2021: 2 · 2019: 3 · 2018: 4 · 2017: 8 · 2016: 4 · 2015: 2 · 2014: 2 · 2013: 1
FWCI
2.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Computational and Mathematical Methods in Medicine (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Science Council (NSC 100-2221-E-259-030-MY2, NSC 101-2221-E-259-012, 100-2221-E-008-008-MY2, 101T924-3, 102T931-3, NSC 101-2911-I-008-001)
  • National Dong Hwa University (NSC 100-2221-E-259-030-MY2, NSC 101-2221-E-259-012, 100-2221-E-008-008-MY2, 101T924-3, 102T931-3, NSC 101-2911-I-008-001)
  • National Central University (NSC 100-2221-E-259-030-MY2, NSC 101-2221-E-259-012, 100-2221-E-008-008-MY2, 101T924-3, 102T931-3, NSC 101-2911-I-008-001)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
Keywords
type 2 diabetes, aging, RR interval, pulse transit time, cardiovascular system, uncontrolled diabetes
MeSH terms
Adolescent, Adult, Aged, Aged, 80 and over, Aging, Cardiovascular Physiological Phenomena, Computer Simulation, Diabetes Mellitus, Type 2, Electrocardiography, Heart Rate, Humans, Middle Aged, Models, Biological, Models, Cardiovascular, Computational Biology, Young Adult, Pulse Wave Analysis

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