Physiological signals as output of the human complex system are typically nonlinear and non-stationary, and much information is hidden in the dynamics of their fluctuations. By applying conventional analysis techniques based on averaged quantities and other features of histogram and classical power spectrum analysis, some important characteristic properties of the signal dynamics are neglected...

Our investigations strongly indicate that in particular the scaling behaviour of the fluctuations plays an important role for distinguishing between healthy and pathological cases.

By adapting and extending recent methods developed in modern statistical physics and nonlinear mathematics, it has been shown that the fluctuations of heartbeat and other neurally-controlled signals exhibit unanticipated hidden information (order) in the form of self-similarity, scaling structure, multifractality and long-term memory.

In the present project we suggest to extend our previous research on the properties of each particular signal. The physiological functions to be studied are the cardio vascular capacities, brain activity (EEG), motor control, gait, posture, sleep and sympathetic and parasympathetic effects.

Algorithms to assess the long-term effects and Inter- relations between signals representing these functions are at the core of this proposal. The simultaneous collection of several long-term signals will enable the construction of physiological networks using dynamical synchronization and cross-correlations patterns, whose momentary state together with the properties of each signal can give a picture of the health status of an individual.