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Yuan, S., Nguyen, M.H., Blitz, P., French, B., Fisk, S., De La Torre, O., Smailagic, A., Siewiorek, D.P., Al Absi, M., Ertin, E., Kamarck, T. and Kumar, S. (2010) Personalised Stress Detection from Physiological Measurements, 2010. International Symposium on Quality of Life Technology Proceedings.

has been cited by the following article:

• 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.