標題: | Inherent Fuzzy Entropy for the Improvement of EEG Complexity Evaluation |
作者: | Cao, Zehong Lin, Chin-Teng 電機工程學系 腦科學研究中心 Department of Electrical and Computer Engineering Brain Research Center |
關鍵字: | Complexity;electroencephalogram (EEG);empirical mode decomposition (EMD);entropy;fuzzy |
公開日期: | 1-Apr-2018 |
摘要: | 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. |
URI: | http://dx.doi.org/10.1109/TFUZZ.2017.2666789 http://hdl.handle.net/11536/144750 |
ISSN: | 1063-6706 |
DOI: | 10.1109/TFUZZ.2017.2666789 |
期刊: | IEEE TRANSACTIONS ON FUZZY SYSTEMS |
Volume: | 26 |
起始頁: | 1032 |
結束頁: | 1035 |
Appears in Collections: | Articles |