Recent Advances in Information Theory Application to Physiological Signals

Dear Colleagues,

Beyond its original home in communication engineering, Information Theory (IT) has recently found a multitude of applications in an emerging area, bioengineering. Scientifically, it is very natural to consider some physiological systems as communication channels, and so analyze their information content. For instance, this has been particularly successful for neural systems. However, IT has also been applied to systems that are not designed to function as communication devices. In this case, the concepts involved only require well-defined probability distributions. Within this context, in the last years, metrics based on a wide variety of entropy, divergence, and distance definitions have been used to reveal clinically useful information from physiological recordings, such as electrocardiograms (ECG), electroencephalograms (EEG), electromyograms (EMG), or intracranial pressure (ICP) signals, among others. Similarly, the application of these concepts to medical images obtained from computed tomography (CT), medical resonance imaging (MRI) or positron emission tomography (PET) has also allowed to develop fast, reliable and accurate algorithms for their segmentation and registration. Moreover, it is worth noting that the information provided in this way is mainly related to underlying mechanisms, which cannot be quantified directly by clinicians in an exploratory examination, thus, providing a significant knowledge increase of diseases that still represent a clinical challenge.

The main goal of this Special Issue is to disseminate new and original research based on the use of IT concepts in clinical contexts to assist in the proper diagnosis and therapeutical decision-making of complex diseases. Nonetheless, manuscripts summarizing the most recent state-of-the-art of this topic will also be welcome.

Prof. Dr. Raúl Alcaraz Martínez
Guest Editor

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