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A Multi-Module Case Based Biofeedback System for Stress Treatment

Publication Type:

Journal article

Venue:

Artificial Intelligence in Medicine

Publisher:

ELSEVIER


Abstract

Biofeedback is today a recognized treatment method for a number of physical and psychological problems. Experienced clinicians often achieve good results in these areas and their success largely builds on many years of experience and often thousands of treated patients. Unfortunately many of the areas where biofeedback is used are very complex, e.g. diagnosis and treatment of stress. Less experienced clinicians may even have difficulties to initially classify the patient correctly. Often there are only a few experts available to assist less experienced clinicians. To reduce this problem we propose a computer assisted biofeedback system helping in classification, parameter setting and biofeedback training. By adopting a case based approach in a computer-based biofeedback system, decision support can be offered to less experienced clinicians and provide a second opinion to experts. We explore how such a system may be designed and validate the approach in the area of stress where the system assists in the classification, parameter setting and finally in the training. In a case study we show that the case based biofeedback system outperforms novice clinicians based on a case library of cases authorized by an expert.

Bibtex

@article{Ahmed1405,
author = {Mobyen Uddin Ahmed and Shahina Begum and Peter Funk and Ning Xiong and Bo von Sch{\'e}ele},
title = {A Multi-Module Case Based Biofeedback System for Stress Treatment},
volume = {51},
number = {2},
pages = {107--115},
month = {February},
year = {2011},
journal = {Artificial Intelligence in Medicine},
publisher = {ELSEVIER},
url = {http://www.es.mdh.se/publications/1405-}
}