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Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation

David Cuesta-Frau1, Pau Miró-Martínez2, Jorge Jordán Núñez2

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Summary

Sample Entropy (SampEn) and Fuzzy Entropy (FuzzyEn) best classify Electroencephalogram (EEG) signals, outperforming Approximate Entropy (ApEn). Noise and muscular artifacts most impact classification accuracy, highlighting the need for robust entropy metrics in EEG analysis.

Keywords:
Approximate EntropyEEG artifactsElectroencephalogramsFuzzy EntropySample EntropySignal classification

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Neuroscience

Background:

  • Entropy metrics quantify signal complexity, crucial for analyzing physiological signals like Electroencephalogram (EEG).
  • First-generation entropy metrics, Approximate Entropy (ApEn) and Sample Entropy (SampEn), are widely used but have limitations.
  • Fuzzy Entropy (FuzzyEn) represents an advancement, aiming for improved performance in signal analysis.

Purpose of the Study:

  • To evaluate and compare the performance of Approximate Entropy (ApEn), Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn) for Electroencephalogram (EEG) signal classification.
  • To assess the robustness of these entropy metrics against common EEG artifacts, including white noise, muscular, cardiac, and ocular artifacts.
  • To determine the optimal initialization parameters for these metrics in the context of EEG artifact contamination.

Main Methods:

  • Utilized two distinct sets of publicly available EEG records.
  • Introduced common EEG artifacts (white noise, muscular, cardiac, ocular) at realistic amplitude ranges.
  • Optimized and assessed the performance and robustness of ApEn, SampEn, and FuzzyEn in classifying artifact-contaminated EEG signals.

Main Results:

  • SampEn and FuzzyEn demonstrated superior performance in EEG signal classification compared to ApEn.
  • White noise and muscular artifacts were identified as the most significant confounding factors affecting classification accuracy.
  • Significant variability was observed in the performance based on the initialization parameters of the entropy metrics.
  • ApEn exhibited poor performance, suggesting its unsuitability for EEG signal classification in the presence of artifacts.

Conclusions:

  • SampEn and FuzzyEn are recommended over ApEn for EEG signal classification tasks, particularly when dealing with artifact contamination.
  • The choice of initialization parameters significantly influences the performance of entropy metrics, necessitating careful optimization.
  • Further research is needed to develop more robust entropy metrics or preprocessing techniques to mitigate the impact of artifacts on EEG analysis.