Working on The Noltisalis Database: Measurement of Nonlinear Properties in Heart Rate Variability Signals

Abstract

We present results obtained from the analysis of 50 heart rate variability, series (HRV) which have been extracted from Holter recordings in the 24 hours in normal subjects and pathological patients. Data have been collected inside a multicentric research program, which aimed at the nonlinear analysis of HRV series. Multifractal approaches such as generalized structure functions have been used to characterize the HRV signal. Moreover, classical parameters for the analysis of the HRV signal over long time scales have been considered to perforin a proper comparison. We considered classical time-domain indexes, "monofractal" characteristics (1/f alpha spectrum; detrended fluctuation analysis) and a regularity, statistic (approximate entropy). The hypothesis of nonlinearity for the HRV signal has been verified by computing the generalized structure function on a set of surrogate data (amplitude adjusted surrogate data). In most cases, the multifractal spectrum of the original HRV series significantly differs (t-test), from those obtained from surrogate signals. This result can be associated with the presence of nonlinear correlations in the HRV signal. Moreover, results show that nonlinear parameters can be used to separate normal subjects from patients suffering from cardiovascular diseases.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA411374

Entities

People

  • M. G. Signorini
  • R. Sassi
  • S. Cerutti

Organizations

  • Polytechnic University of Milan

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Biomedical Engineering
  • Cardiovascular Diseases
  • Cardiovascular System
  • Data Science
  • Databases
  • Diseases And Disorders
  • Engineering
  • Frequency Domain
  • Heart Failure
  • Heart Rate
  • Information Science
  • Nonlinear Dynamics
  • Power Spectra
  • Spectra
  • Stationary Processes
  • Stochastic Processes
  • Time Domain

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