What Can Biosignal Entropy Tell Us About Health and Disease? Applications in Some Clinical Fields

Abstract

Many physiological systems are paradigmatic examples of complex networks, displaying behaviors best studied by means of tools derived from nonlinear dynamics and fractal geometry. Furthermore, while conventional wisdom considers health as an 'orderly' situation (and diseases are often called 'disorders'), truth is that health is characterized by a remarkable (pseudo)-randomness, and the loss of this pseudo-randomness (i.e., the 'decomplex-ification' of the system's output) is one of the earliest signs of the system's dysfunction. The potential clinical uses of this information are evident. However, the instruments used to assess complexity are still under debate, and these tools are just beginning to find their place at the bedside. We present a brief overview of the potential uses of complexity analysis in several areas of clinical medicine. We comment on the metrics most frequently used, and we review specifically their application on certain neurologic diseases, aging, diabetes, febrile diseases and the critically ill patient.