Differentiation of Heart Rate Time Series from Electroencephalogram and Noise
Analysis of heart rate variability (HRV) has become a
popular non-invasive tool for assessing the activities of autonomic
nervous system. Most of the methods were hired from techniques
used for time series analysis. Currently used methods are time
domain, frequency domain, geometrical and fractal methods. A new
technique, which searches for pattern repeatability in a time series, is
proposed for quantifying heart rate (HR) time series. These set of
indices, which are termed as pattern repeatability measure and
pattern repeatability ratio are able to distinguish HR data clearly
from noise and electroencephalogram (EEG). The results of analysis
using these measures give an insight into the fundamental difference
between the composition of HR time series with respect to EEG and
noise.
[1] Bhattacharya J., "Complexity analysis of spontaneous EEG," Acta.
Neurobiol. Exp,. vol. 60, pp. 495-50, 2000
[2] Faber R., Baumert M., Stepan H., Wessel N., Voss A. and Walther T.
"Baroreflex sensitivity, heart rate, and blood pressure variability in
hypertensive pregnancy disorders," Journal of Human Hypertension, vol.
18, 707-712, 2004.
[3] Parati G, Frattola A, Omboni S, Mancia G, and Di Rienzo M., "Analysis
of heart rate and blood pressure variability in the assessment of
autonomic regulation in arterial hypertension," Clin Sci. (Colch), vol. 91
Supl., pp. 129-132, 1996.
[4] 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.
[5] 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.
[6] 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.
[7] 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,
[8] Fuan Sztajzel, "Heart rate variability: a noninvasive electrocardiographic
method to measure the autonomic nervous system," Swiss Med. Wkly.,
vol. 134, pp. 514 - 522, 2004.
[9] 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.
[10] 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.
[11] 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.
[12] Burioka N et al. Approximate Entropy in the Electroencephalogram
During Wake and Sleep. Clinical EEG and Neuroscience 2005; 36: 1.
[13] Jordi Almitras, "Understanding of autonomic sympathovagal balance
from short-term heart rate variations. Are we analyzing noise,"
Comparative Biochemistry and Physiology, Part A 124, 447-460, 1999.
[14] 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.
[15] 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.
[16] Otakar Fojt and Jiri Holcik, "Applying nonlinear dynamics to ECG
signal processing," IEEE Engg. Med. Biol. Soc., pp. 96-101,
March/April 1998.
[17] 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
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] www.physionet.org
[23] V. I. Thajudin Ahamed, P. Dhanasekharan, N. G. Karthick, Pual K.
Joseph, A. Naseem, and T. K. Abdul Jaleel, "A novel pattern recognition
technique for quantification of heart rate variability," Proceedings of the
international conference on biomedical and pharmaceutical engineering,
ICBPE, Singapore, 2006, pp. 110 - 113
[1] Bhattacharya J., "Complexity analysis of spontaneous EEG," Acta.
Neurobiol. Exp,. vol. 60, pp. 495-50, 2000
[2] Faber R., Baumert M., Stepan H., Wessel N., Voss A. and Walther T.
"Baroreflex sensitivity, heart rate, and blood pressure variability in
hypertensive pregnancy disorders," Journal of Human Hypertension, vol.
18, 707-712, 2004.
[3] Parati G, Frattola A, Omboni S, Mancia G, and Di Rienzo M., "Analysis
of heart rate and blood pressure variability in the assessment of
autonomic regulation in arterial hypertension," Clin Sci. (Colch), vol. 91
Supl., pp. 129-132, 1996.
[4] 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.
[5] 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.
[6] 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.
[7] 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,
[8] Fuan Sztajzel, "Heart rate variability: a noninvasive electrocardiographic
method to measure the autonomic nervous system," Swiss Med. Wkly.,
vol. 134, pp. 514 - 522, 2004.
[9] 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.
[10] 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.
[11] 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.
[12] Burioka N et al. Approximate Entropy in the Electroencephalogram
During Wake and Sleep. Clinical EEG and Neuroscience 2005; 36: 1.
[13] Jordi Almitras, "Understanding of autonomic sympathovagal balance
from short-term heart rate variations. Are we analyzing noise,"
Comparative Biochemistry and Physiology, Part A 124, 447-460, 1999.
[14] 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.
[15] 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.
[16] Otakar Fojt and Jiri Holcik, "Applying nonlinear dynamics to ECG
signal processing," IEEE Engg. Med. Biol. Soc., pp. 96-101,
March/April 1998.
[17] 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
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] www.physionet.org
[23] V. I. Thajudin Ahamed, P. Dhanasekharan, N. G. Karthick, Pual K.
Joseph, A. Naseem, and T. K. Abdul Jaleel, "A novel pattern recognition
technique for quantification of heart rate variability," Proceedings of the
international conference on biomedical and pharmaceutical engineering,
ICBPE, Singapore, 2006, pp. 110 - 113
@article{"International Journal of Electrical, Electronic and Communication Sciences:64292", author = "V. I. Thajudin Ahamed and P. Dhanasekaran and Paul Joseph K.", title = "Differentiation of Heart Rate Time Series from Electroencephalogram and Noise", abstract = "Analysis of heart rate variability (HRV) has become a
popular non-invasive tool for assessing the activities of autonomic
nervous system. Most of the methods were hired from techniques
used for time series analysis. Currently used methods are time
domain, frequency domain, geometrical and fractal methods. A new
technique, which searches for pattern repeatability in a time series, is
proposed for quantifying heart rate (HR) time series. These set of
indices, which are termed as pattern repeatability measure and
pattern repeatability ratio are able to distinguish HR data clearly
from noise and electroencephalogram (EEG). The results of analysis
using these measures give an insight into the fundamental difference
between the composition of HR time series with respect to EEG and
noise.", keywords = "Approximate entropy, heart rate variability, noise,pattern repeatability, and sample entropy.", volume = "2", number = "1", pages = "166-4", }
