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Methods Inf Med 2004; 43(01): 118-121
DOI: 10.1055/s-0038-1633847
Original Article
Schattauer GmbH

Characterization of Heart Rate Variability Changes Following Asphyxia in Rats

L. Moraru
1   Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece
,
L. Cimponeriu
1   Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece
,
S. Tong
2   Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
,
N. Thakor
2   Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
,
A. Bezerianos
1   Department of Medical Physics, School of Medicine, University of Patras, Patras, Greece
› Author Affiliations
Further Information

Publication History

Publication Date:
07 February 2018 (online)

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Summary

Objectives: A non-invasive method to monitor the functioning of the autonomous nervous system consists in heart rate variability (HRV) analysis. The aim of this study was to investigate the changes on HRV after an asphyxia experiment in rats, using several linear (time and frequency domain) and nonlinear parameters (approximate entropy, SD1 and SD2 indices derived from Poincare plots).

Methods: The experiments involved the study of HRV changes after cardiac arrest (CA) resulting from 5 min of hypoxia and asphyxia, followed by manual resuscitation and return of spontaneous circulation. 5 min stationary periods of RR intervals were selected for further analysis from 5 rats in following distinct situations: 1) baseline, 2) 30 min after CA, 3) 60 min after CA, 4) 90 min after CA, 5) 120 min after CA, 6) 150 min after CA. The ANS contribution has been delineated based on time and frequency domain analysis.

Results and Conclusions: The results indicate that the recovery process following the asphyxia cardiac arrest reflects the impaired functioning of the autonomic nervous system. Both linear and nonlinear parameters track the different phases of the experiment, with an increased sensitivity displayed by the approximate entropy (ApEn). After 150 min the ApEn RRI parameter recovers to its baseline value. The results forward the ApEn as a more sensitive parameter of the recovery process following the asphyxia.

Keywords

Heart rate variability - autonomous nervous system - linear and nonlinear analysis - approximate entropy
 

  • References

  • 1 Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability. Standards of measurement, physiological interpretation and clinical use. Circulation 1996; 93 (05) 1043-65.
    CrossrefPubMedGoogle Scholar
  • 2 Geocadin R, Ghodadra R, Kimura K, Lei H, Sherman D, Hanley D, Thakor N. A novel quantitative EEG injury measure of global cerebral ischemia. Clinical Neurophysiology 2000; 111: 1779-87.
    CrossrefPubMedGoogle Scholar
  • 3 Korhonen I, Mainardi LT, Yppärilä H, Musialowicz T. Comparison of linear and non-linear analysis of heart rate variability in sedated cardiac surgery patients. Proceedings of 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Istanbul, Turkey: October 25-28 2001
    Google Scholar
  • 4 Aubert EA, Ramaekers D, Beckers F, Breem R, Denef C, Van-de-Werf F, Ector H. The analysis of heart rate variability in unrestrained rats Validation of method and results. Computer Methods and Programs in Biomedicine 1999; 60 (03) 197-213.
    CrossrefPubMedGoogle Scholar
  • 5 Troncoso E, Rodriguez M, Feria M. Light induced arousal affects simultaneously EEG and heart rate variability in the rat. Neurosci Lett 1995; 188: 167-70.
    CrossrefPubMedGoogle Scholar
  • 6 Pagani M, Lombardi F, Guzzetti S, Rimoldi O, Furlan R, Pizzinelli P, Sandrone G, Malfatto G, Dell’Orto S, Piggaluga E, Turiel M, Baselli G, Cerutti S, Malliani A. 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: 178-93.
    CrossrefPubMedGoogle Scholar
  • 7 Eckberg D. Sympathovagal balance. A critical appraisal. Circulation 1997; 96: 3224-32.
    CrossrefPubMedGoogle Scholar
  • 8 Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 1991; 88: 2297-301.
    CrossrefPubMedGoogle Scholar
  • 9 Cysarz D, Bettermann H, Van Leeuwen P. Entropies of short binary sequences in heart period dynamics. Am J Physiol 2000; 278 (06) H2163-H2172.
    PubMedGoogle Scholar
  • 10 Tulppo MP, Mäkikallio TH, Takala TE, Seppänen T, Huikuri HV. Quantitative beattobeat analysis of heart rate dynamics during exercise. Am J Physiol 1996; 271: H244-H252.
    PubMedGoogle Scholar