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Comparative study between Sample Entropy and Detrended Fluctuation Analysis performance on EEG records under data

E M Cirugeda-Roldán1, A Molina-Picó, D Cuesta-Frau

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This study compared Sample Entropy (SampEn) and Detrended Fluctuation Analysis (DFA) on EEG signals. DFA better segments epileptic signals with data loss, while SampEn excels with control signals.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Electroencephalography (EEG) signal analysis is crucial for diagnosing neurological disorders.
  • Entropy measures like Sample Entropy (SampEn) and Detrended Fluctuation Analysis (DFA) are used to characterize EEG complexity.
  • Robustness of these measures to signal artifacts, such as sample loss, is critical for clinical application.

Purpose of the Study:

  • To compare the performance of Sample Entropy (SampEn) and Detrended Fluctuation Analysis (DFA) in analyzing real EEG signals.
  • To evaluate the efficacy of SampEn and DFA under conditions of randomized sample removal.
  • To determine which entropy measure is more suitable for specific EEG signal types and diagnostic contexts.

Main Methods:

  • Real EEG signals were analyzed using both Sample Entropy (SampEn) and Detrended Fluctuation Analysis (DFA).
  • Randomized sample removal was introduced to simulate data loss and assess measure robustness.
  • The ability of each measure to differentiate between various EEG conditions was quantitatively assessed.

Main Results:

  • Sample Entropy (SampEn) demonstrated superior performance in analyzing control EEG signals.
  • Detrended Fluctuation Analysis (DFA) provided better segmentation results for epileptic EEG signals, especially with sample loss.
  • DFA was particularly effective in distinguishing between seizure and seizure-free intervals in epileptic EEG data.

Conclusions:

  • The choice between SampEn and DFA depends on the specific EEG analysis task and signal characteristics.
  • DFA offers greater robustness in segmenting epileptic seizure intervals when faced with data loss.
  • SampEn is more effective for analyzing normal or control EEG signals, even with minor data imperfections.