Information Theory and Complexity Science Approaches to Health Conditions and Cognitive Decline

Dear Colleagues,

The application of information theory to investigate the structural complexity of physiological signals is one of the fastest growing areas in multidisciplinary research, and has the potential to transform lifestyles and healthcare. The related assessment of the deterministic vs stochastic behaviour of the observables also comes with an enormous potential to distinguish between the pathological and a healthy functioning of human organs.

A whole host of approaches do exist to characterise the underlying structural complexity within physiological signals, ranging from entropy-based algorithms to chaotic attractors. These are being gradually applied with the aim to enhance our understanding of the governing mechanisms of the human body, while the so-enabled quantitative assessment of the state of body and mind has enormous potential in transforming both diagnostic methods and the management of chronic illnesses. The scope of using information theory to analyse the (mal-)functioning of the human body is vast, ranging from congestive heart failure, depression, to hydrocephalus and cognitive decline. However, these analyses are still in their infancy, and this Special Issue is an attempt to share the findings on information theory aspects of the assessment of the structural complexity of physiological signals.

The brain, in particular, still remains one of the least understood organs, yet the need to unlock the mysteries of the human brain has never been so pressing. The ageing of the population has led to a rise in cases of cognitive decline, while modern lifestyles have led another pressing issue of chronic stress and depressions experienced by large portions of the population. It is therefore both imperative and timely to explore further the potential of information theory in the early quantification, prevention and management of these issues.

This Special Issue aims to disseminate the latest findings in the investigation and characterisation of health conditions and cognitive decline, using information theory and nonlinear complexity science approaches. We welcome manuscripts presenting novel findings that promise to revolutionise the health and cognitive sciences, in addition to those introducing novel algorithms to quantify the degree of a health-related condition through the assessment of the structural complexity of pathological signals. In particular, we encourage submissions on data acquired from wearable devices, such as ‘hearables’, which are very convenient for the user, but record typically weaker signals, with an overall signal quality that is compromised (due to artefacts or noise).

Prof. Dr. Danilo P. Mandic
Dr. Theerasak Chanwimalueang
Dr. Tricia Adjei
Guest Editor

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