The Compound Indexing of Human Self-Similar Behavioural Patterns

Applications of a constitutive framework providing compound complexity analysis and indexing of coarse-grained self-similar time series representing behavioural data are presented. A notion of behavioural entropy and hysteresis is introduced as two different forms of compound measures. These measures provide clinically applicable complexity analysis of behavioural patterns yielding scalar characterisation of time-varying behaviours registered over an extended period of time. The behavioural data are obtained using body attached sensors providing non-invasive readings of heart rate, skin blood perfusion, blood oxygenation, skin temperature, movement and steps frequency. The results using compound measures of behavioural patterns of fifteen healthy individuals are presented. The application of the compound measures is shown to correlate with complexity analysis. The correlation is demonstrated using two healthy subjects compared against a control group. This indicates a possibility to use these measures in place of fractional dimensions to provide a finer characterisation of behavioural patterns observed using sensory data acquired over a long period of time.

The Compound Spectral Indices of Human Stress

Temporally fine-grained and objective measures of mental states or their surrogate states are desperately needed in clinical psychiatry. Stress, both acute and especially chronic stress, is an important mental and physiological state observed in many mental disorders. It is a potential precipitant of acute psychiatric decompensations, be they anxious, affective, psychotic, or behavioural. Thus, being able to objectively follow stress or its surrogate parameters over time in a clinician-friendly way would help predict and prevent decompensations and monitor subsequent treatment success. Thus, we introduce the Compound Spectral Stress Indices (CSSI) that are derived from sensing data of various physiological and physiological and behavioural parameters we use as surrogate stress measures. To obtain the CSSI we use a hierarchical approach provided by adaptability, congruency and derived stress coefficient matrices. Adaptability is defined as a macroscopic characterisation of physiological and physiological and behavioural performance constructed as a product of the total variation of time-segmented complexity indices multiplied by the frequency of the time-varying distribution of complexity indices of the measured physiological or physiological and behavioural parameters, where complexity is expressed in terms of the Hurst exponent. Congruency is expressed by a constant characterising a demand-resource balance and it is then expressed in the form of a stress coefficient matrix. The CSSI is given by the spectral distance of the stress coefficient matrices from the ideal demand-resource matrix.

On the possibility of identifying human subjects using behavioural complexity analyses

Identification of human subjects using a geometric approach to complexity analysis of behavioural data is designed to provide a basis for a more precise diagnosis leading towards personalised medicine. Methods： The approach is based on capturing behavioural time-series that can be characterized by a fractional dimension using non-invasive longer-time acquisitions of heart rate, perfusion, blood oxygenation, skin temperature, relative movement and steps frequency. The geometry based approach consists in the analysis of the area and centroid of convex hulls encapsulating the behavioural data represented in Euclidian index spaces based on the scaring properties of the self-similar normally distributed behavioural time-series of the above mentioned quantities. Results： An example demonstrating the presented approach of behavioural fingerprinting is provided using sensory data of eight healthy human subjects based on approximately fifteen hours of data acquisition. Our results show that healthy subjects can be factorized to different similarity groups based on a particular choice of a convex hull in the corresponding Euclidian space. One of the results indicates that healthy subjects share only a small part of the convex hull pertaining to a highly trained individual from the geometric comparison point of view. Similarly, the presented pair-wise individual geometric similarity measure indicates large differences among the subjects suggesting the possibility of neuro-fingerprinting. Conclusions： Recently introduced multi-channel body-attached sensors provide a possibility to acquire behavioural time-series that can be mathematically analysed to obtain various objective measures of behavioural patterns yielding behavioural diagnoses favouring personalised treatments of, e.g., neuropathologies or aging.