AM  Vol.9 No.12 , December 2018
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.
Cite this paper: Kloucek, P. and Gunten, A. (2018) The Compound Spectral Indices of Human Stress. Applied Mathematics, 9, 1378-1394. doi: 10.4236/am.2018.912090.

[1]   McEwen, B.S. (2017) Neurobiological and Systemic Effects of Chronic Stress. Chronic Stress, 1, 2470547017692328.

[2]   Liew, W.S., Seera, M., Loo, C.K., Lim, E. and Kubota, N. (2016) Classifying Stress from Heart Rate Variability Using Salivary Biomarkers as Reference. IEEE Transactions on Neural Networks and Learning Systems, 27, 2035-2046.

[3]   Hellhammer, D.H., Wust, S. and Kudielka, B.M. (2009) Salivary Cortisol as a Biomarker in Stress Research. Psycho-Neuro-Endocrinology, 34, 163-171.

[4]   Webster, J.G. (1997) Design of Pulse Oximeters. Series in Medical Physics and Biomedical Engineering. CRC Press.

[5]   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.

[6]   Wijsman, J., Grundlehner, B., Liu, H., Hermens, H. and Penders, J. (2011) Towards Mental Stress Detection Using Wearable Physiological Sensors. Engineering in Medicine and Biology Society, EMBC, Annual International Conference of the IEEE, 1798-1801.

[7]   Yoon, S., Sim, J.K. and Cho, Y.-H. (2016) A Flexible and Wearable Human Stress Monitoring Patch. Scientific Reports, 6.

[8]   Bassingthwaighte, J.B., Liebovitch, L.S. and West, B.J. (1994) Fractal Physiology. American Physiological Society: The American Physiological Society Methods in Physiology Series. American Physiological Society.

[9]   Koolhaas, J.M., Bartolomucci, A., Buwalda, B., de Boer, S.F., Flugge, G., Korte, S.M., Meerlo, P., Murison, R., Olivier, B., Palanza, P., Richter-Levin, G., Sgoifo, A., Steimer, T., Stiedl, O., van Dijk, G., Wohr, M. and Fuchs, E. (2011) Stress Revisited: A Critical Evaluation of the Stress Concept. Neuroscience & Biobehavioral Reviews, 35, 1291-1301.

[10]   Kloucek, P., Zakharov, P. and von Gunten, A. (2016) The Compound Indexing of Human Self-Similar Behavioural Patterns. Journal of Applied Mathematics, 7, 2212-2228.

[11]   Kloucek, P. and von Gunten, A. (2016) On the Possibility of Identifying Human Subjects Using Behavioural Complexity Analyses. Quantitative Biology, 4, 261-269.

[12]   Porges, S.W. (1992) Vagal Tone: A Physiologic Marker of Stress Vulnerability. Pediatrics, 90, 498-504.

[13]   Lee, J., Hwang, Y., Cheon, K. and Jung, H. (2012) Emotion-on-a-Chip (eoc): Evolution of Bio Chip Technology to Measure Human Emotion Using Body Fluids. Medical Hypotheses, 79, 827-832.

[14]   Cicchetti, D. (2010) Resilience under Conditions of Extreme Stress: A Multilevel Perspective. World Psychiatry, 9, 145-154.

[15]   Holden, J.G., Riley, M.A., Gao, J. and Torre, K. (2013) Fractal Analyses: Statistical and Methodological Innovations and Best Practices. Frontiers in Physiology, 4, 97.

[16]   Van Ness, M. (1968) Fractional Brownian Motions, Fractional Noise and Application. SIAM Review, 10, 422-437.