Combined heart rate variability and dynamic measures for quantitatively characterizing the cardiac stress status during cycling exercise

Computers in Biology and Medicine

Volumn 63, Issue , 2015, Pages 133-142

  Chen, Szi Wen ^a   Liaw, Jiunn Woei ^a   Chang, Ya Ju ^a   Chuang, Li Ling ^a   Chien, Chun Tse ^a  

^a CHANG GUNG UNIVERSITY   (Taiwan)

Author keywords

Cardiac stress status; Cycling exercise; Detrended fluctuation analysis (DFA); Fractal; Heart rate variability (HRV)

Indexed keywords

DISCRIMINANT ANALYSIS; ELECTROCARDIOGRAPHY; FEATURE EXTRACTION; FRACTALS; REGRESSION ANALYSIS; SPECTRAL DENSITY; SPORTS; STATISTICS; TIME SERIES ANALYSIS;

BORG RATING OF PERCEIVED EXERTIONS; CARDIAC STRESS; CYCLING EXERCISE; DETRENDED FLUCTUATION ANALYSIS; HEART RATE VARIABILITY; LINEAR DISCRIMINANT ANALYSIS; MULTIVARIATE LINEAR REGRESSION ANALYSIS; POWER SPECTRAL DENSITIES (PSD);

HEART;

ARTICLE; CYCLING; DISCRIMINANT ANALYSIS; ELECTROCARDIOGRAPHY; EXERCISE; EXERCISE TEST; HEART RATE; HEART RATE VARIABILITY; HEART STRESS; HUMAN; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RUNNING; SPECTROMETRY; ADULT; BIOLOGICAL MODEL; FEMALE; HEART; MALE; PHYSIOLOGY;

ADULT; EXERCISE TEST; FEMALE; HEART; HEART RATE; HUMANS; MALE; MODELS, CARDIOVASCULAR;

EID: 84931268994     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2015.05.026     Document Type: Article

References (23)

1. Chen S.W., Liaw J.W., Chan H.L., Chang Y.J., Ku C.H. A real-time fatigue monitoring and analysis system for lower extremity muscles with cycling movement. Sensors 2014, 14:12410-12424.
2. Borg G.A. Psychophysical bases of perceived exertion. Med. Sci. Sports Exerc. 1982, 14:377-381.
3. Chang Y.J., Liu C.C., Lin C.H., Tsaih P.L., Hsu M.J. Using electromyography to detect the weightings of the local muscle factors to the increase of perceived exertion during stepping exercise. Sensors 2008, 8:3643-3655.
4. Stamford B. Validity and reliability of subjective ratings of perceived exertion during work. Ergonomics 1976, 19:53-60.
5. Matthews S., Jelinek H.F., Vafaeiafraz S., McLachlan C.S. Heart rate stability and decreased parasympathetic heart rate variability in healthy young adults during perceived stress. Int. J. Cardiol. 2012, 156:337-338.
6. Cornforth D.J., Robinson D.J., Spence I., Jelinek H.F. Heart rate recovery in decision support for high performance athlete training schedules. Interdiscip. J. Inf. Knowl. Manag. 2014, 9:193-207.
7. Chen S.W. A new algorithm developed based on a mixture of spectral and nonlinear techniques for the analysis of heart rate variability. J. Med. Eng. Technol. 2007, 31:210-219.
8. Akselrod S., Gordon D., Ubel F.A., Shannon D.C., Berger A.C., Cohen R.J. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 1981, 213:220-222.
9. Pagani M., Lombardi F., Guzzetti S., Rimoldi O., Furlan R., Pizzinelli P., Sandrone G., Malfatto G., Dell'Orto S., Piccaluga E. Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circ. Res. 1986, 59:178-193.
10. Laguna P., Moody G.B., Mark R.G. Power spectra density of unevenly sampled data by least-square analysis: performance and application to heart rate signals. IEEE Trans. Biomed. Eng. 1998, 45:698-715.
11. Chen S.W., Chao S.C. Compressed sensing technology-based spectral estimation of heart rate variability using the integral pulse frequency modulation model. IEEE J. Biomed. Health Inform. 2014, 18:1081-1090.
12. Chen S.W., Chao S.C. A reweighted ℓ1-minimization based compressed sensing for the spectral estimation of heart rate variability using the unevenly sampled data. PLoS One 2014, 9:e99098.
13. Peng C.K., Havlin S., Stanley H.E., Goldberger A.L. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 1995, 5:82-87.
14. Karavirta L., Tulppo M.P., Laaksonen D.E., Nyman K., Laukkanen R.T., Kinnunen H., Häkkinen A., Häkkinen K. Heart rate dynamics after combined endurance and strength training in older men. Med. Sci. Sports Exerc. 2009, 41:1436-1443.
15. Huikuri H.V., Makikallio T.H., Peng C., Goldberger A.L., Hintze U., Moller M. Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction. Circulation 2000, 101:47-53.
16. Tulppo M.P., Hughson R.L., Makikallio T.H., Airaksinen K.E., Seppanen T., Huikuri H.V. Effects of exercise and passive head-up tilt on fractal and complexity properties of heart rate dynamics. Am. J. Physiol. Heart Circ. Physiol. 2001, 280:1081-1087.
17. Pikkujamsa S.M., Makikallio T.M., Sourannder L.B., Räihä I.J., Puukka P., Skyttä J., Peng C.K., Goldberger A.L., Huikuri H.V. Cardiac interbeat interval dynamics from childhood to senescence: comparison of conventional and new measures based on fractals and chaos theory. Circulation 1999, 100:393-399.
18. Rodriguez E., Echeverria J.C., Alvarez-Ramirez J. Detrended fluctuation analysis of heart intrabeat dynamics. Physica A 2007, 384:429-438.
19. Chen Z., Ivanov P.Ch., Hu K., Stanley H.E. Effect of nonstationarities on detrended fluctuation analysis. Phys. Rev. E 2002, 65:041107.
20. Guzmán-Vargas L., Reyes-Ramírez I., Hernández-Pérez R., Angulo-Brown F F. Scaling differences of heartbeat excursions between wake and sleep periods. Methods Enzymol. 2011, 487:409-429.
21. Munoz-Diosdado A., Guzmán-Vargas L., Ramírez-Rojas A., Del Rio-Correa J.L., Angulo-Brown F. Some cases of crossover behavior in heart interbeat and electroseismic time series. Fractals 2005, 13:253-263.
22. Perini R., Veicsteinas A. Heart rate variability and autonomic activity at rest and during exercise in various physiological conditions. Eur. J. Appl. Physiol. 2003, 90:317-325.
23. Ferrario M., Moissl U., Garzotto F., Cruz D.N., Tetta C., Signorini M.G., Ronco C., Grassmann A., Cerutti S., Guzzetti S. The forgotten role of central volume in low frequency oscillations of heart rate variability. PLoS One 2015, 10:e0120167.