A refined method of quantifying deceleration capacity index for heart rate variability analysis

Biomed Eng Online. 2018 Dec 18;17(1):184. doi: 10.1186/s12938-018-0618-x.

Abstract

Background: Phase-rectified signal averaging (PRSA) was often applied to assess the cardiac vagal modulation. Despite its broad use, this method suffers from the confounding effects of anomalous variants of sinus rhythm. This study aimed to improve the original PRSA method in deceleration capacity (DC) quantification.

Methods: The refined deceleration capacity (DCref) was calculated by excluding from non-vagally mediated abnormal variants of sinus rhythms. Holter recordings from 202 healthy subjects and 51 patients with end-stage renal disease (ESRD) have been used for validity. The DCref was compared to original DC (DCorg) by the area under receiver operating characteristic curve.

Results: Experimental results demonstrate that the original and refined DCs calculated from 24-h, 2-h, and 30-min Holter recordings are significantly lower in patients with ESRD than those in the healthy group. In receiver operating characteristic curve analysis, the DCref provides better performance than the DCorg in distinguishing between the patients with ESRD and healthy control subjects. Furthermore, the refined PRSA technique enhances the low frequency and attenuates high frequency components for spectral analysis in ESRD patients.

Conclusions: The DCref appears to reduce the influence of non-vagally mediated abnormal variants of sinus rhythm and highlighting the pathological influence. DCref, especially assessed from short-term electrocardiography recordings, may be complementary to existing autonomic function assessment, risk stratification, and efficacy prediction strategies.

Keywords: Autonomic nervous system; Deceleration capacity; Heart rate variability; Phase-rectified signal averaging.

MeSH terms

  • Adult
  • Case-Control Studies
  • Deceleration*
  • Electrocardiography*
  • Female
  • Heart Rate*
  • Humans
  • Kidney Failure, Chronic / physiopathology
  • Male
  • Middle Aged
  • Signal Processing, Computer-Assisted*