Estimation of Complexity of Sampled Biomedical Continuous Time Signals Using Approximate Entropy.

ABSTRACT: Non-linear analysis found many applications in biomedicine. Approximate entropy (ApEn) is a popular index of complexity often applied to biomedical data, as it provides quite stable indications when processing short and noisy epochs. However, ApEn strongly depends on parameters, which were chosen in the literature in wide ranges. This paper points out that ApEn depends on sampling rate of continuous time signals, embedding dimension, tolerance (under which a match is identified), epoch duration and low frequency trends. Moreover, contradicting results can be obtained changing parameters. This was found both in simulations and in experimental EEG. These limitations of ApEn suggest the introduction of an alternative index, here called modified ApEn, which is based on the following principles

SUBMITTER: Mesin L 

PROVIDER: S-EPMC6004374 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature