Key result
Fuzzy entropy (FuzEn) demonstrated better consistency than sample entropy (SampEn) in short time series for both simulations and real EEG data during nociception assessment.
Why the study?
Refined multiscale entropy normally uses sample entropy, which depends on data length and standard deviation, prompting evaluation of fuzzy entropy and its refinements as alternatives.
Fuzzy entropy provides better consistency than sample entropy for refined multiscale entropy applications in short time series, such as EEG-based nociception assessment.
May aid short EEG nociception assessment; leaves open prospective validation before clinical adoption.
The refined multiscale entropy (RMSE) approach is commonly applied to assess complexity as a function of the time scale. RMSE is normally based on the computation of sample entropy (SampEn) estimating complexity as conditional entropy. However, SampEn is dependent on the length and standard deviation of the data. Recently, fuzzy entropy (FuzEn) has been proposed, including several refinements, as an alternative to counteract these limitations. In this work, FuzEn, translated FuzEn (TFuzEn), translated-reflected FuzEn (TRFuzEn), inherent FuzEn (IFuzEn), and inherent translated FuzEn (ITFuzEn) were exploited as entropy-based measures in the computation of RMSE and their performance was compared to that of SampEn. FuzEn metrics were applied to synthetic time series of different lengths to evaluate the consistency of the different approaches. In addition, electroencephalograms of patients under sedation-analgesia procedure were analyzed based on the patient's response after the application of painful stimulation, such as nail bed compression or endoscopy tube insertion. Significant differences in FuzEn metrics were observed over simulations and real data as a function of the data length and the pain responses. Findings indicated that FuzEn, when exploited in RMSE applications, showed similar behavior to SampEn in long series, but its consistency was better than that of SampEn in short series both over simulations and real data. Conversely, its variants should be utilized with more caution, especially whether processes exhibit an important deterministic component and/or in nociception prediction at long scales.
No takes yet. Share an insight, caveat, or question.
Valencia et al. (2019) studied Nociception assessment during sedation-analgesia. Fuzzy entropy (FuzEn) metrics vs. Sample entropy (SampEn) was evaluated on Consistency of entropy-based measures in short and long series. Fuzzy entropy (FuzEn) demonstrated better consistency than sample entropy (SampEn) in short time series for both simulations and real EEG data during nociception assessment.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: