TITLE:
The Compound Spectral Indices of Human Stress
AUTHORS:
Petr Kloucek, Armin Von Gunten
KEYWORDS:
Stress, Physiological and Behavioural Complexity, Surrogate Data, Self-Similar Normally Distributed Processes, Hurst Exponent
JOURNAL NAME:
Applied Mathematics,
Vol.9 No.12,
December
28,
2018
ABSTRACT: 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.