Three different measurement protocols were set for better understanding the performance of DistEn, which are: i) calculate the DistEn of a specific EEG signal using the full recording ii) calculate the DistEn by averaging the results for all its possible non-overlapped 5 second segments and iii) calculate it by averaging the DistEn values for all the possible non-overlapped segments of 1 second length, respectively. The publicly-accessible Bonn database which consisted of normal, interictal, and ictal EEG signals was used in this study. We thus aimed, in the present study, to show the potential of DistEn in the analysis of epileptic EEG signals. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with the conventional entropy methods for especially short length data. Recently published studies have made elaborate attempts to distinguish between the normal and epileptic EEG signals by advanced nonlinear entropy methods, such as the approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. It is an open-ended challenge to accurately detect the epileptic seizures through electroencephalogram (EEG) signals.
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