Entropy-Based Algorithms in the Analysis of Biomedical Signals

Biomedical signals are frequently noisy and incomplete. They produce complex and high-dimensional data sets. In these mentioned cases, the results of traditional methods of signal processing can be skewed by noise or interference present in the signal. Information entropy, as a measure of disorder or uncertainty in the data, was introduced by Shannon. To date, many different types of entropy methods have appeared with many different application areas. The purpose of this paper is to present a short overview of some methods of entropy analysis and to discuss their suitability for use in the analysis of biomedical signals.