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A novel encoding Lempel-Ziv complexity algorithm for quantifying the irregularity of physiological time series.

Yatao Zhang1, Shoushui Wei2, Hai Liu3

  • 1School of Control Science and Engineering, Shandong University, Jinan 250061, China; School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, China.

Computer Methods and Programs in Biomedicine
|July 10, 2016
PubMed
Summary

The novel encoding LZ (ELZ) complexity algorithm accurately measures physiological time series irregularity. Unlike classic LZ (CLZ) and multistate LZ (MLZ), ELZ distinguishes irregularity from chaos and is more stable with longer sequences.

Keywords:
Coarse-graining processEncoding LZ complexityLempel–Ziv complexityPhysiological time series

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Area of Science:

  • Physiological time series analysis
  • Biomedical signal processing
  • Complexity science

Background:

  • Lempel-Ziv (LZ) complexity is widely used for analyzing physiological time series irregularity.
  • Existing LZ variants struggle to differentiate between mere irregularity and true chaotic characteristics.
  • A need exists for improved algorithms to accurately quantify physiological signal complexity.

Purpose of the Study:

  • To compare the performance of a novel encoding LZ (ELZ) complexity algorithm against classic LZ (CLZ) and multistate LZ (MLZ) algorithms.
  • To evaluate the ability of ELZ to specifically capture signal irregularity.
  • To assess the robustness and sensitivity of ELZ in analyzing physiological data.

Main Methods:

  • Developed and implemented the encoding LZ (ELZ) complexity algorithm.
  • Compared ELZ with CLZ and MLZ using simulated time series (Gaussian noise, chaotic, periodic).
  • Analyzed the impact of sequence length and performed sensitivity analysis on all algorithms.
  • Applied ELZ to cardiac interbeat (RR) interval time series from the MIT-BIH database.

Main Results:

  • ELZ monotonically declined with reduced irregularity, unlike CLZ and MLZ which showed overlapping values for chaotic and noisy data.
  • ELZ demonstrated superior accuracy in capturing signal irregularity, distinguishing it from complexity.
  • ELZ exhibited greater stability with longer sequence lengths (>300) compared to CLZ and MLZ.
  • ELZ and MLZ were sensitive to changes in time sequences, while CLZ was not.
  • ELZ accurately measured RR interval irregularity, showing lower values in congestive heart failure patients (p < 0.01).

Conclusions:

  • The encoding LZ (ELZ) algorithm is a more accurate and reliable measure of physiological time series irregularity than CLZ and MLZ.
  • ELZ effectively differentiates signal irregularity from chaotic dynamics.
  • ELZ shows promise for clinical applications, such as in analyzing cardiac health through RR interval variability.