Quantification of Heart Rate Variability: A Measure based on Unique Heart Rates
It is established that the instantaneous heart rate (HR) of healthy humans keeps on changing. Analysis of heart rate variability (HRV) has become a popular non invasive tool for assessing the activities of autonomic nervous system. Depressed HRV has been found in several disorders, like diabetes mellitus (DM) and coronary artery disease, characterised by autonomic nervous dysfunction. A new technique, which searches for pattern repeatability in a time series, is proposed specifically for the analysis of heart rate data. These set of indices, which are termed as pattern repeatability measure and pattern repeatability ratio are compared with approximate entropy and sample entropy. In our analysis, based on the method developed, it is observed that heart rate variability is significantly different for DM patients, particularly for patients with diabetic foot ulcer.
[1] S. K. Ramchurn, D. Baijnath, and A. Murray, "Low-dimentional chaotic
behaviour in heart rate variability," Computers in Cardiology, vol. 27,
pp. 473-476, 2000.
[2] R. U. Acharya, A. Kumar, P. S. Bhat, C. M. Lim, S. S. Iyengar, N.
Kannathai, and S. M. Krishnan, "Classification of cardiac abnormalities
using heart rate signals," Med. Biol. Eng. Comput. , vol. 42, pp. 288-
293, 2004.
[3] G. Krsacic, A. Krsacic, M. Martinis, E. Vargovic, A. Knezevic, A.
Smalcelj, M. Jemberk-Gostovic, D. Gamberger, and T. Smuc, "Nonlinear
analysis of heart rate variability in patients with coronary heart
disease," Computers in Cardiology, vol. 29, pp. 673-675, 2002.
[4] Rajendra Acharya U., Kannathal N. Ong Wai Sing, Luk Yi Ping, and
TjiLeng Chua, "Heart rate analysis of normal subjects in various age
groups," Biomedical Engineering Online, vol. 3 no. 24, 2004,
[5] Fuan Sztajzel, "Heart rate variability: a noninvasive electrocardiographic
method to measure the autonomic nervous system," Swiss Med. Wkly.,
vol. 134, pp. 514 - 522, 2004.
[6] D. Cysarz, P. Van Leeuwen and H. Bettermann, "Irregularities and
nonlinearities in fetal heart period time series in the course of
pregnancy," Herzschr Elektrophys, vol. 11, pp. 127-130, 2000.
[7] R. U. Acharya, C. Lim, and P. Joseph, "Heart rate variability analysis
using correlation dimension and detrended fluctuation analysis," ITBMRBM,
vol. 23, 333-339, 2002.
[8] Paul Joseph, U Rajendra Acharya, Chua Kok Poo. Johny Chee, Lim
Choo Min, S S Iyengar, and Hock Wei, "Effect of reflexological
stimulation on heart rate variability," ITBM-RBM, vol. 25, 40-45, 2004.
[9] Siddharth N. Shah, Ed. in chief, API Text Book of Medicine, 7th edition,
The association of physiscians of India, 2003, pp. 1120-1131.
[10] Andrew J. M. Boulton, Arthur I. Vinik, Joseph C. Arezzo, Vera Bril,
Eva L. Feldman, Roy Freeman, Rayaz A. Malik, Raelene E. Maser, Jay
M. Sosenko, and Dan Ziegler, "Diabetic neuropathies, a statement by
American diabetes association," Diabetes Care, vol. 28, 956-962, 2005.
[11] Nikolaos Kadoglou, and Christos Trontzos, "The contribution of SPET
123I-MIBG scintigraphy to the diagnosis and prognosis of diabetic
cardiac autonomic neuropathy," Hell. J. Nucl. Med., vol. 7, no. 2, 71-77,
2004.
[12] Valentin Fuster, R. Wayne Alexander, and Robert A. Rourke, Hurst-s
The Heart, 11th Edition, McGH medical publishing division, vol. 1,
2004, pp. 822.
[13] Task force of the European Society of Cardiology and North American
Society of Pacing and Electrophysiology, "Heart rate variability,
standards of measurement, physiological interpretation, and clinical
use," Circ., vol. 93. no. 5, pp. 1043-1065, 1996.
[14] P. Van Leeuwen and H. Bettermann, "The status of nonlinear dynamics
in the analysis of heart rate variability, editorial," Herzschr Elektrophys,
vol. 11, pp. 127-130, 2000.
[15] Otakar Fojt and Jiri Holcik, "Applying nonlinear dynamics to ECG
signal processing," IEEE Engg. Med. Biol. Soc., pp. 96-101,
March/April 1998.
[16] S.M. Pincus. Approximate entropy as a measure of system complexity.
Proc. Natl. Acad. Sci. USA, vol. 88, no. 6, 2297-2301, March 15,1991
[17] J. S. Richmann and J. R. Moormann "Physiological time-series analysis
using approximate entropy and sample entropy," Am. J.. Physiol. Heart
Circ. Physiol., vol. 278, H2039 - H2049, 2000.
[18] Douglas E. Lake, Joshua S. Richman, M. Pamela Griffin, and J. Randall
Moormann, "Sample entropy analysis of neonatal heart rate variability,"
Am. J. Physiol. Regu.l Integr. Comp. Physiol,, vol. 283, pp. 789-797,
2002.
[19] M. Costa, A. L. Goldberger, and C. K. Peng, "Multiscale entropy
analysis of complex physiological time series," Physical Revew Letters,
vol. 89 no. 6, Aug. 2002.
[20] M Costa, C. K. Peng A. L Goldberger and J. M. Housdorff, "Multiscale
entropy analysis of human gait dynamics," Physica A, vol. 330, pp. 53-
60, 2003.
[21] www.physionet.org
[1] S. K. Ramchurn, D. Baijnath, and A. Murray, "Low-dimentional chaotic
behaviour in heart rate variability," Computers in Cardiology, vol. 27,
pp. 473-476, 2000.
[2] R. U. Acharya, A. Kumar, P. S. Bhat, C. M. Lim, S. S. Iyengar, N.
Kannathai, and S. M. Krishnan, "Classification of cardiac abnormalities
using heart rate signals," Med. Biol. Eng. Comput. , vol. 42, pp. 288-
293, 2004.
[3] G. Krsacic, A. Krsacic, M. Martinis, E. Vargovic, A. Knezevic, A.
Smalcelj, M. Jemberk-Gostovic, D. Gamberger, and T. Smuc, "Nonlinear
analysis of heart rate variability in patients with coronary heart
disease," Computers in Cardiology, vol. 29, pp. 673-675, 2002.
[4] Rajendra Acharya U., Kannathal N. Ong Wai Sing, Luk Yi Ping, and
TjiLeng Chua, "Heart rate analysis of normal subjects in various age
groups," Biomedical Engineering Online, vol. 3 no. 24, 2004,
[5] Fuan Sztajzel, "Heart rate variability: a noninvasive electrocardiographic
method to measure the autonomic nervous system," Swiss Med. Wkly.,
vol. 134, pp. 514 - 522, 2004.
[6] D. Cysarz, P. Van Leeuwen and H. Bettermann, "Irregularities and
nonlinearities in fetal heart period time series in the course of
pregnancy," Herzschr Elektrophys, vol. 11, pp. 127-130, 2000.
[7] R. U. Acharya, C. Lim, and P. Joseph, "Heart rate variability analysis
using correlation dimension and detrended fluctuation analysis," ITBMRBM,
vol. 23, 333-339, 2002.
[8] Paul Joseph, U Rajendra Acharya, Chua Kok Poo. Johny Chee, Lim
Choo Min, S S Iyengar, and Hock Wei, "Effect of reflexological
stimulation on heart rate variability," ITBM-RBM, vol. 25, 40-45, 2004.
[9] Siddharth N. Shah, Ed. in chief, API Text Book of Medicine, 7th edition,
The association of physiscians of India, 2003, pp. 1120-1131.
[10] Andrew J. M. Boulton, Arthur I. Vinik, Joseph C. Arezzo, Vera Bril,
Eva L. Feldman, Roy Freeman, Rayaz A. Malik, Raelene E. Maser, Jay
M. Sosenko, and Dan Ziegler, "Diabetic neuropathies, a statement by
American diabetes association," Diabetes Care, vol. 28, 956-962, 2005.
[11] Nikolaos Kadoglou, and Christos Trontzos, "The contribution of SPET
123I-MIBG scintigraphy to the diagnosis and prognosis of diabetic
cardiac autonomic neuropathy," Hell. J. Nucl. Med., vol. 7, no. 2, 71-77,
2004.
[12] Valentin Fuster, R. Wayne Alexander, and Robert A. Rourke, Hurst-s
The Heart, 11th Edition, McGH medical publishing division, vol. 1,
2004, pp. 822.
[13] Task force of the European Society of Cardiology and North American
Society of Pacing and Electrophysiology, "Heart rate variability,
standards of measurement, physiological interpretation, and clinical
use," Circ., vol. 93. no. 5, pp. 1043-1065, 1996.
[14] P. Van Leeuwen and H. Bettermann, "The status of nonlinear dynamics
in the analysis of heart rate variability, editorial," Herzschr Elektrophys,
vol. 11, pp. 127-130, 2000.
[15] Otakar Fojt and Jiri Holcik, "Applying nonlinear dynamics to ECG
signal processing," IEEE Engg. Med. Biol. Soc., pp. 96-101,
March/April 1998.
[16] S.M. Pincus. Approximate entropy as a measure of system complexity.
Proc. Natl. Acad. Sci. USA, vol. 88, no. 6, 2297-2301, March 15,1991
[17] J. S. Richmann and J. R. Moormann "Physiological time-series analysis
using approximate entropy and sample entropy," Am. J.. Physiol. Heart
Circ. Physiol., vol. 278, H2039 - H2049, 2000.
[18] Douglas E. Lake, Joshua S. Richman, M. Pamela Griffin, and J. Randall
Moormann, "Sample entropy analysis of neonatal heart rate variability,"
Am. J. Physiol. Regu.l Integr. Comp. Physiol,, vol. 283, pp. 789-797,
2002.
[19] M. Costa, A. L. Goldberger, and C. K. Peng, "Multiscale entropy
analysis of complex physiological time series," Physical Revew Letters,
vol. 89 no. 6, Aug. 2002.
[20] M Costa, C. K. Peng A. L Goldberger and J. M. Housdorff, "Multiscale
entropy analysis of human gait dynamics," Physica A, vol. 330, pp. 53-
60, 2003.
[21] www.physionet.org
@article{"International Journal of Medical, Medicine and Health Sciences:59642", author = "V. I. Thajudin Ahamed and P. Dhanasekaran and A. Naseem and N. G. Karthick and T. K. Abdul Jaleel and Paul K.Joseph", title = "Quantification of Heart Rate Variability: A Measure based on Unique Heart Rates", abstract = "It is established that the instantaneous heart rate (HR) of healthy humans keeps on changing. Analysis of heart rate variability (HRV) has become a popular non invasive tool for assessing the activities of autonomic nervous system. Depressed HRV has been found in several disorders, like diabetes mellitus (DM) and coronary artery disease, characterised by autonomic nervous dysfunction. A new technique, which searches for pattern repeatability in a time series, is proposed specifically for the analysis of heart rate data. These set of indices, which are termed as pattern repeatability measure and pattern repeatability ratio are compared with approximate entropy and sample entropy. In our analysis, based on the method developed, it is observed that heart rate variability is significantly different for DM patients, particularly for patients with diabetic foot ulcer.
", keywords = "Autonomic nervous system, diabetes mellitus, heart
rate variability, pattern identification, sample entropy", volume = "2", number = "3", pages = "106-4", }
