It is well known that biomedical signals, such as heart rate variability (HRV), electrocardiogram (ECG), electroencephalogram (EEG), and voice, arise from complex nonlinear dynamical systems, as cardiovascular, nervous, or phonatory systems. Information extracted from these signals provides insights regarding the status of the underlying systems. Complexity measures are helpful to quantitatively describe nonlinear biomedical systems and to detect changes in their dynamics that can be associated with physiological or pathological events. These measures on biomedical signals and images can be used in a wide field of applications, as pathology detection, decision support systems, treatment monitoring, and temporal segmentation and, in the study, characterization of the underlying biomedical systems. However, in the practice, many challenges emerge when these complexity measures are applied, such as the influence of the noise, the quantization effects, the lengths of the available data, or the parameters tuning. How to cope with these difficulties and how to obtain tools that can be employed in clinical practice are the subjects of this special issue.

This special issue is focused not only on the application of existing complexity measures on biomedical signals and images but also on the development of new complexity measure algorithms.

Combinations with machine learning based strategies, automatization in parameter setting, and applications in pattern recognition problems are specially encouraged, as well as developments and applications of novel complexity estimators for multivariate, multiscale, or multimodal data.

Potential topics include but are not limited to the following:

• Correlation dimension, correlation entropy, and Kolmogorov entropy estimations
• Approximate and sample entropies
• Shannon, Rényi, and Tsallis entropies
• Permutation and dispersion entropies
• Multiscale entropy measures
• Multiresolution and spectral complexity measures
• Multivariate complexity measures
• Fractal and multifractal analysis
• Lempel-Ziv complexity
• Nonlinear synchronisation measures
• Kullback-Leibler divergence, Jensen-Shannon divergence, and mutual information