Skip to main content

Advertisement

Log in

Enhancing the deceleration capacity index of heart rate by modified-phase-rectified signal averaging

  • Technical Note
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Deceleration capacity (DC) of heart rate is a novel indicator of autonomic nervous system (ANS) activity. In this paper, we proposed a modified DC index based on improved phase-rectified signal averaging (PRSA) algorithm. Sinusoidal analysis is applied to elucidate the rationality of the improved PRSA. Then the validity of the modified DC is verified by the databases of chronic heart failure (CHF) patients and control group. Both the conventional and modified DCs are significantly lower in CHF patients than that in the control group (2.12 ± 2.98 vs. 6.34 ± 1.92 ms, P < 0.0001 and 5.45 ± 2.48 vs. 10.64 ± 1.76 ms, P < 0.0001, respectively). And the modified DC provides higher accuracy in distinguishing CHF than the conventional one (87.4 vs. 82.1%). The results indicate that the suggested technique enhances the performance of PRSA and improves the efficiency of DC in assessing ANS activity in CHF patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Bauer A, Kantelhardt JW, Barthel P et al (2006) Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study. Lancet 367:1674–1681

    Article  Google Scholar 

  2. Bauer A, Kantelhardt JW, Bunde A et al (2006) Phase-rectified signal averaging detects quasi-periodicities in non-stationary data. Physica A 364:423–434

    Article  Google Scholar 

  3. Bauer A, Barthel P, Schneider R et al (2009) Improved stratification of autonomic regulation for risk prediction in post-infarction patients with preserved left ventricular function (ISAR-Risk). Eur Heart J 30:576–583. doi:10.1093/eurheartj/ehn540

    Article  Google Scholar 

  4. Bonaduce D, Petretta M, Marciano F et al (1999) Independent and incremental prognostic value of heart rate variability in patients with chronic heart failure. Am Heart J 138:273–284

    Article  Google Scholar 

  5. Chen XX, Mukkamala R (2008) Selective quantification of the cardiac sympathetic and parasympathetic nervous systems by multisignal analysis of cardiorespiratory variability. Am J Physiol Heart Circ Physiol 294:H362–H371. doi:10.1152/ajpheart.01061.2007

    Article  Google Scholar 

  6. Guyton AC, Hall JE (2000) Textbook of medical physiology. W. B. Saunders, Philadelphia

    Google Scholar 

  7. Kantelhardt JW, Bauer A, Schumann AY et al (2007) Phase-rectified signal averaging for the detection of quasi-periodicities and the prediction of cardiovascular risk. Chaos 17:015112

    Article  Google Scholar 

  8. Lemay M, Prudat Y, Jacquemet V et al (2008) Phase-rectified signal averaging used to estimate the dominant frequencies in ECG signals during atrial fibrillation. IEEE Trans Biomed Eng 55:2538–2547

    Article  Google Scholar 

  9. Li M, Zheng C, Sato T et al (2004) Vagal nerve stimulation markedly improves long-term survival after chronic heart failure in rats. Circulation 109:120–124. doi:10.1161/01.cir.0000105721.71640.da

    Article  Google Scholar 

  10. McMurray JJV, Pfeffer MA (2005) Heart failure. Lancet 365:1877–1889

    Article  Google Scholar 

  11. Miyamoto S, Fujita M, Sekiguchi H et al (2001) Effects of posture on cardiac autonomic nervous activity in patients with congestive heart failure. J Am Coll Cardiol 37:1788–1793

    Article  Google Scholar 

  12. Mortara A, Tavazzi L (1996) Prognostic implications of autonomic nervous system analysis in chronic heart failure: role of heart rate variability and baroreflex sensitivity. Arch Gerontol Geriatr 23:265–275

    Article  Google Scholar 

  13. Mortara A, La Rovere MT, Pinna GD et al (1997) Arterial baroreflex modulation of heart rate in chronic heart failure: clinical and hemodynamic correlates and prognostic implications. Circulation 96:3450–3458

    Google Scholar 

  14. Olshansky B, Sabbah HN, Hauptman PJ et al (2008) Parasympathetic nervous system and heart failure: pathophysiology and potential implications for therapy. Circulation 118:863–871. doi:10.1161/circulationaha.107.760405

    Article  Google Scholar 

  15. Piskorski J, Guzik P (2007) Geometry of the poincare plot of RR intervals and its asymmetry in healthy adults. Physiol Meas 28:287–300

    Article  Google Scholar 

  16. Schumann AY, Kantelhardt JW, Bauer A et al (2008) Bivariate phase-rectified signal averaging. Physica A 387:5091–5100

    Article  Google Scholar 

  17. Szabo BM, van Veldhuisen DJ, vander Veer N et al (1997) Prognostic value of heart rate variability in chronic congestive heart failure secondary to idiopathic or ischemic dilated cardiomyopathy. Am J Cardiol 79:978–980

    Article  Google Scholar 

  18. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996) Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 93:1043–1065

    Google Scholar 

  19. Zhong YR, Jan KM, Ju KH et al (2006) Quantifying cardiac sympathetic and parasympathetic nervous activities using principal dynamic modes analysis of heart rate variability. Am J Physiol Heart Circ Physiol 291:H1475–H1483. doi:10.1152/ajpheart.00005.2006

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Nature Science Foundation of China (Grant 30570483) and the Science and Technology Department of Zhejiang Province, China (Grant 2006C13018).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jing Yan or Gangmin Ning.

Appendix

Appendix

A theoretical derivation is presented in the appendix for calculating the number of pseudo anchor points in one period of a discrete sinusoid.

The discrete sinusoid is given as \( x_{n} = \sin \left( {2\pi n{\frac{a}{b}} + \delta } \right) \), n = 1, …, N. The sample points categorized to pseudo anchor points follow two criteria:

  1. (1)

    The sample points locate on the decreasing edge;

  2. (2)

    Each point is larger than the former one.

The two criteria lead to a relationship among the index of the sample points, the normalized frequency and the initial phase. Phases of the points follow criterion (1) correspond to:

$$ {\frac{1}{2}}\,\pi + 2k\pi < 2\pi n{\frac{a}{b}} + \delta < {\frac{3}{2}}\pi + 2k\pi . $$
(7)

It can be further derived as:

$$ {\frac{b\pi + 4kb\pi - 2\delta b}{4a\pi }} < n < {\frac{3b\pi + 4kb\pi - 2\delta b}{4a\pi }} $$
(8)

and the phases of the points follow criterion (2) correspond to:

$$ \sin \left( {2\pi n{\frac{a}{b}} + \delta } \right) > \sin \left( {2\pi (n - 1){\frac{a}{b}} + \delta } \right). $$
(9)

It can be simplified as:

$$ 2\cos \left( {2\pi n{\frac{a}{b}} - {\frac{a}{b}}\pi + \delta } \right)\sin \left( {{\frac{a}{b}}\pi } \right) > 0. $$
(10)

Since a and b are positive integers and a < b (because b is the period of the sinusoid), the range of \( {\frac{a}{b}}\pi \) is limited in (0, π) and thus \( \sin \left( {{\frac{a}{b}}\pi } \right) > 0. \) So \( \cos \left( {2\pi n{\frac{a}{b}} - {\frac{a}{b}}\pi + \delta } \right) > 0. \) The range of the phase is given as:

$$ \begin{gathered} 2k\pi < 2\pi n{\frac{a}{b}} - {\frac{a}{b}}\pi + \delta < {\frac{\pi }{2}} + 2k\pi \hfill \\ {\text{or}} \hfill \\ {\frac{3}{2}}\pi + 2k\pi < 2\pi n{\frac{a}{b}} - {\frac{a}{b}}\pi + \delta < 2\pi + 2k\pi . \hfill \\ \end{gathered} $$
(11)

The relationship among n and a, b, δ is obtained as:

$$ \begin{gathered} {\frac{2kb\pi + a\pi - \delta b}{2a\pi }} < n < {\frac{4kb\pi + b\pi + 2a\pi - 2\delta b}{4a\pi }} \hfill \\ {\text{or}} \hfill \\ {\frac{4kb\pi + 3b\pi + 2a\pi - 2\delta b}{4a\pi }} < n < {\frac{2kb\pi + 2b\pi + a\pi - \delta b}{2a\pi }} \hfill \\ \end{gathered} $$
(12)

In both (8) and (12), k ranges from 0 to a−1. In conclusion, when the index of the sample point, the normalized frequency, and the initial phase satisfy with (8) and (12), this sample point is a pseudo anchor point.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pan, Q., Gong, Y., Gong, S. et al. Enhancing the deceleration capacity index of heart rate by modified-phase-rectified signal averaging. Med Biol Eng Comput 48, 399–405 (2010). https://doi.org/10.1007/s11517-010-0589-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-010-0589-y

Keywords

Navigation