Advanced processing algorithms (Analysis)

Advanced processing algorithms (Analysis)

 

  • Wavelet-based ECG detector/delineator: main beat waves (P, Q, R, S, T) and their onset and offset can be extracted.  Several performances are available: single lead, multi-lead based on vectorcardiogram (VCG) and multi-lead applying post-processing rules to ensure the reliability of each mark [J.P. Martínez et al, 2004].
  • Heart beat classifier: Normal, supraventricular and ventricular beats can be identified by this classifier. Classification can be done by three performances: fully automatic, slightly-assisted and fully-assisted [M. Llamedo and J. P. Martínez, 2010].
  • PPG pulse detector: analogously of detecting main beat waves, in photoplestimographic signals the detection of each pulse is the first step for further analysis [E. Gil et al, 2008].
  • Ischemic detector: a root mean squared over ST-T segment evaluated in the long term ST Database in three protocols. The study was driven on long recordings 21- 24 hours duration and therefore the reliability of its application on other recordings is not ensured [A. Mincholé et al, 2005].
  • T-Wave alternans detector: The quantification of T-Wave alternans is based on a multilead approach carrying out the estimation of the amplitude of the alternans [V. Monasterio and J.P. Martínez, 2008].
  • ECG-derived respiration (EDR):
    • VCG-based: This algorithm exploits the oscillatory pattern of the rotation angles of the heart’s electrical axis as induced by respiration. At least three orthogonal leads are needed to apply this method since it is based on the vectorcardiogram (VCG) representation [R. Bailón et al, 2006].
    • QRS slopes-based: This technique is based on the variations of the slopes of the QRS complexes for downward and upward slopes. Respiratory rate estimation consists in two phases: power spectrum estimation and “peak-conditioned” averaging [J. Lázaro et al, 2013].
  • Heart rate turbulence (HRT): A Neyman-Pearson approach is used to detect and characterize HRT after ventricular premature beats.  Turbulence onset (TO) and slope (TS) are obtained from the RR intervals [J.P. Martínez et al, 2008].
  • Baroreflex sensitivity analysis: events and sequence techniques are available to assess the baroreflex sensitivity, nevertheless that is only possible if arterial blood pressure and ECG is acquired in the same recording [S. Gouveia et al, 2008].
  • Heart rate variability analysis (HRV): Time and frequency domain for stationary signals are provided. Moreover, the time variant HRV analysis is also included by smoothed pseudo Wigner-Ville distribution [M. Orini et al, 2010].   Parametric methods such as autoregressive (AR) or ARARX are also available to HRV analysis.
  • Heart rate variability non-linear analysis: approximate and sample entropy and correlation dimension are the non-linear methods included for assessing the HRV from the chaos theory point of view [J. Bolea et al, 2014].
  • Ventricular repolarisation adaptation to heart rate changes:  QT series (QRS onset to T wave offset) remains the ventricular repolarisation of the heart while RR means the heart rate.  An abrupt change in the heart rhythm is followed by a change in the ventricular repolarisation with a certain delay [E. Pueyo et al, 2004], which is related with arrhythmic risk.

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