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Inherent fuzzy entropy for the improvement of EEG complexity evaluation
journal contributionposted on 2023-05-20, 02:04 authored by Cao, Z, Lin, C-T
In recent years, the concept of entropy has been widely used to measure the dynamic complexity of signals. Since the state of complexity of human beings is significantly affected by their health state, developing accurate complexity evaluation algorithms is a crucial and urgent area of study. This paper proposes using inherent fuzzy entropy (Inherent FuzzyEn) and its multiscale version, which employs empirical mode decomposition and fuzzy membership function (exponential function) to address the dynamic complexity in electroencephalogram (EEG) data. In the literature, the reliability of entropy-based complexity evaluations has been limited by superimposed trends in signals and a lack of multiple time scales. Our proposed method represents the first attempt to use the Inherent FuzzyEn algorithm to increase the reliability of complexity evaluation in realistic EEG applications. We recorded the EEG signals of several subjects under resting condition, and the EEG complexity was evaluated using approximate entropy, sample entropy, FuzzyEn, and Inherent FuzzyEn, respectively. The results indicate that Inherent FuzzyEn is superior to other competing models regardless of the use of fuzzy or nonfuzzy structures, and has the most stable complexity and smallest root mean square deviation.
Publication titleIEEE Transactions on Fuzzy Systems
Department/SchoolSchool of Information and Communication Technology
PublisherIeee-Inst Electrical Electronics Engineers Inc
Place of publication445 Hoes Lane, Piscataway, USA, Nj, 08855
Rights statementCopyright 2016 IEEE.