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Including the Magnitude Variability of a Signal in the Ordinal Pattern Analysis.

Melvyn Tyloo1,2, Joaquín González3, Nicolás Rubido4

  • 1Living Systems Institute, University of Exeter, Exeter EX4 4QD, UK.

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Summary
This summary is machine-generated.

This study introduces a novel method to enhance signal analysis by incorporating signal magnitudes lost in Ordinal Pattern (OP) encoding. This approach improves characterization and aids AI classifiers by providing complementary features for better accuracy.

Keywords:
feature extractionordinal patternspermutation entropysignal analysis

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Area of Science:

  • Signal processing
  • Complexity science
  • Machine learning

Background:

  • Ordinal Patterns (OPs) are a popular method for signal analysis, transforming signals into symbolic sequences.
  • OPs are conceptually clear, simple to implement, robust to noise, and applicable to short signals.
  • A major drawback of OPs is the loss of information regarding signal magnitudes during encoding.

Purpose of the Study:

  • To propose a method that utilizes signal magnitudes discarded during OP encoding.
  • To use these magnitudes as a complementary variable to permutation entropy for improved signal characterization.
  • To demonstrate the utility of this approach for feature engineering and enhancing AI classifiers.

Main Methods:

  • Developing a technique to recover and utilize signal magnitudes lost in the OP encoding process.
  • Combining permutation entropy with the variability of signal magnitudes.
  • Applying the enhanced method to synthetic (logistic and Hénon maps) and real-world signals (EEG, power grids).

Main Results:

  • The proposed method improves signal characterization when permutation entropy is complemented with signal magnitude variability.
  • Results remain explainable, demonstrating the practical applicability of the approach.
  • The enhanced features derived from this method can improve the accuracy of AI classifiers.

Conclusions:

  • The novel approach effectively overcomes the information loss drawback of traditional OP encoding.
  • Complementing permutation entropy with signal magnitude variability offers a more comprehensive signal analysis.
  • This method holds significant potential for feature engineering and advancing machine learning applications in signal processing.