A multi-module case-based biofeedback system for stress treatment

  • Authors:

  • Mobyen Uddin Ahmed;Shahina Begum;Peter Funk;Ning Xiong;Bo von Scheele

  • Affiliations:

  • School of Innovation, Design and Engineering, Mälardalen University, P.O. Box 883, SE-721 23, Västerås, Sweden;School of Innovation, Design and Engineering, Mälardalen University, P.O. Box 883, SE-721 23, Västerås, Sweden;School of Innovation, Design and Engineering, Mälardalen University, P.O. Box 883, SE-721 23, Västerås, Sweden;School of Innovation, Design and Engineering, Mälardalen University, P.O. Box 883, SE-721 23, Västerås, Sweden;School of Innovation, Design and Engineering, Mälardalen University, P.O. Box 883, SE-721 23, Västerås, Sweden

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

Objective: 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. Methods: The decision support system (DSS) analysis finger temperature in time series signal where the derivative of temperature in time is calculated to extract the features. The case-based reasoning (CBR) is used in three modules to classify a patient, estimate parameters and biofeedback. In each and every module the CBR approach retrieves most similar cases by comparing a new finger temperature measurement with previously solved measurements. Three different methods are used to calculate similarity between features, they are: modified distance function, similarity matrix and fuzzy similarity. Results and conclusion: We explore how such a DSS can be designed and validated the approach in the area of stress where the system assists in the classification, parameter setting and finally in the training. In this case study we show that the case based biofeedback system outperforms trainee clinicians based on a case library of cases authorized by an expert.