Abstract
Symbolic entropy is proposed to measure the complexity of the electroencephalogram (EEG) signal under different brain functional states. The EEG data recorded from different subjects were investigated and compared with both approximate entropy (ApEn) and Shannon entropy. The experimental results show that the proposed method can effectively distinguish the complexities of two groups. The experimental results provide preliminary support for the notion that the complex nonlinear nature of brain electrical activity may be the result of isolation or impairment of the neural information transmission within the brain. It is concluded that symbolic entropy serves a better measure for EEG signals and other medical signals.
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Sun, L., Yu, J., Beadle, P.J. (2009). A Novel Method for Analyzing Dynamic Complexity of EEG Signals Using Symbolic Entropy Measurement. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_58
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DOI: https://doi.org/10.1007/978-3-642-01513-7_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01512-0
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