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Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
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Testing for nonlinearity in non-stationary physiological time series.

Diego Guarín1, Edilson Delgado, Álvaro Orozco

  • 1Department of Electrical Eng,Universidad Tecnológica de Pereira, Vereda la Julita, Pereira, Colombia. dlguarin@gmail.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary

This study introduces a new method for detecting nonlinearity in non-stationary time series, improving upon traditional linear surrogate data methods. The approach accurately identifies nonlinear patterns in complex physiological signals like heart rate variability (HRV).

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Area of Science:

  • Time Series Analysis
  • Signal Processing
  • Biomedical Engineering

Background:

  • Nonlinearity testing is crucial for nonlinear time series analysis.
  • Traditional linear surrogate data methods are unreliable for non-stationary signals, common in physiology.
  • Non-stationary physiological signals can lead to false nonlinearity detection.

Purpose of the Study:

  • To propose a novel methodology for assessing nonlinearity in non-stationary time series.
  • To extend constrained surrogate time series generation for improved nonlinearity detection.
  • To address the limitations of linear surrogate methods in analyzing physiological data.

Main Methods:

  • Developed a methodology based on band-phase-randomized surrogates.
  • This method randomizes a portion of Fourier phases in the high-frequency domain, unlike linear methods.
  • Applied the methodology to simulated time series and heart rate variability (HRV) records.

Main Results:

  • The proposed method successfully discriminates between linear stationary, linear non-stationary, and nonlinear time series.
  • It outperforms the standard linear surrogate data method for non-stationary data.
  • Nonlinear correlations were detected in non-stationary HRV signals from healthy patients.

Conclusions:

  • The band-phase-randomized surrogate method offers a robust approach for nonlinearity assessment in non-stationary time series.
  • This technique is suitable for analyzing complex physiological signals like HRV.
  • The findings confirm the presence of nonlinear dynamics in human heart rate variability.