Complexity analysis of the biomedical signal using fuzzy entropy measurement

Exploiting the concept of fuzzy sets, a new time series complexity measure named fuzzy entropy was developed. In fuzzy entropy, the degree of similarity between vectors is defined on the basis of a fuzzy membership function and according to the shapes of the fuzzy membership function rather than by employing the conventional Heaviside function used in approximate entropy and sample entropy. Tests conducted on independent identically distributed uniform random numbers, mixture stochastic processes, the Rossler attractor, the Henon map, and sinusoidal signals showed that fuzzy entropy is superior to sample entropy in several respects, providing entropy definition in the case of small parameters, improving the degree of monotonicity, and being more robust to noise. The results of tests on a biomedical time series of electromyography signals illustrate the applicability of the proposed method.

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