Approaching Mental Disorders from the Engineering Point of View

Authors

  • Jordi Aguiló Department of Microelectronics and Electronic Systems, Universidad Autónoma de Barcelona, Barcelona, Spain Biomedical Research Networking Center (CIBER-BBN), Spain

DOI:

https://doi.org/10.14738/tmlai.54.3651

Keywords:

Mental disorders, Mental Health, Reference Scale of severity, Multivariable biomarker

Abstract

Mental illness and mental disorders represent an increasing burden affectingthepopulation of all ages at all places, challenging mental health and health systems and contributing to the onset or to the acceleration of many other diseases. Cardiovascular risk, diabetes or also depression among many others seems to be clear examples of these effects. Concerning depression, there is a critical cross feedback: Stress causes depression whilethat staying in depressive states causes stress.

An accurate, reliable and almost continuous monitoring of the “instantaneous” state of the subjects would undoubtedly contribute to a better diagnostic and follow-up. So, treatments would be personalized by fine tuning relationships between drugs or other kind of interventions and subject state which in turn would contribute to a better knowledge of the causes of the disease and of the mechanisms acting in its treatment.

Following this primary thought,a research line explicitly focusing quantitative assessment methods and related technologies was stablished at the Universidad Autónoma de Barcelona.

First results had come from the study of moderate stress. In collaboration with various technological and clinical groups we proposed a reference scale to measure the stress level and we have taken the first steps concerning its validation; we are proposing a set of weighed parameters coming from physiological signals as a multivariable biomarker including some unexpected components. This “biomarker” show a strong correlation with the reference scale then stress can be quantitatively assessed continuously, reliably, repeatable and avoiding most of the subjective components of the patient and the observer.From these results, it is expected to demonstrate that a tool is available to determine, for example, the quality of an empowerment session, the effectiveness of an intervention using mindfulness in children with TDAH, or to determine the extent to which biofeedback techniques are useful as tools to improve quality of life of primary caregivers of chronic patients.

Some of the pilots planned at the very beginning are still underway while new research in Depression, Epilepsy, MS and Alzheimer is under way following similar guidelines.


References

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Published

2017-09-01

How to Cite

Aguiló, J. (2017). Approaching Mental Disorders from the Engineering Point of View. Transactions on Machine Learning and Artificial Intelligence, 5(4). https://doi.org/10.14738/tmlai.54.3651

Issue

Section

Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems