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

• Published in: Artificial intelligence in medicine 2011-02, Vol.51 (2), p.107-115
• Main Authors: Ahmed, Mobyen Uddin, Begum, Shahina, Funk, Peter, Xiong, Ning, von Scheele, Bo
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Biofeedback treatment; Biofeedback, Psychology; Body Temperature; Case-based reasoning; Computer Graphics; Decision Support Systems, Clinical; Decision Support Techniques; Diagnosis, Computer-Assisted; Fingers; Fuzzy Logic; Humans; Internal Medicine; Knowledge Bases; Medical Informatics - methods; Multi-module; Other; Sensor-based system; Stress diagnosis; Stress, Psychological - diagnosis; Stress, Psychological - physiopathology; Stress, Psychological - therapy; Systems Integration; Therapy, Computer-Assisted; User-Computer Interface
• ISSN: 0933-3657; 1873-2860; 1873-2860
• DOI: 10.1016/j.artmed.2010.09.003

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.