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A Multi-Module Case Based Biofeedback System for Stress Treatment
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0002-1212-7637
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0002-5562-1424
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0001-9857-4317
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2011 (English)In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 51, no 2, p. 107-115Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2011. Vol. 51, no 2, p. 107-115
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-8950DOI: 10.1016/j.artmed.2010.09.003ISI: 000289331100004PubMedID: 20947318Scopus ID: 2-s2.0-79952512335OAI: oai:DiVA.org:mdh-8950DiVA, id: diva2:301511
Available from: 2010-03-03 Created: 2010-03-03 Last updated: 2018-10-16Bibliographically approved
In thesis
1. A Multimodal Approach for Clinical Diagnosis and Treatment
Open this publication in new window or tab >>A Multimodal Approach for Clinical Diagnosis and Treatment
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A computer-aided Clinical Decision Support System (CDSS) for diagnosis and treatment often plays a vital role and brings essential benefits for clinicians. Such a CDSS could function as an expert for a less experienced clinician or as a second option/opinion of an experienced clinician to their decision making task. Nevertheless, it has been a real challenge to design and develop such a functional system where accuracy of the system performance is an important issue.

This research work focuses on development of intelligent CDSS based on a multimodal approach for diagnosis, classification and treatment in medical domains i.e. stress and post-operative pain management domains. Several Artificial Intelligence (AI) techniques such as Case-Based Reasoning (CBR), textual Information Retrieval (IR), Rule-Based Reasoning (RBR), Fuzzy Logic and clustering approaches have been investigated in this thesis work.

Patient’s data i.e. their stress and pain related information are collected from complex data sources for instance, finger temperature measurements through sensor signals, pain measurements using a Numerical Visual Analogue Scale (NVAS), patient’s information from text and multiple choice questionnaires. The proposed approach considers multimedia data management to be able to use them in CDSSs for both the domains.

The functionalities and performance of the systems have been evaluated based on close collaboration with experts and clinicians of the domains. In stress management, 68 measurements from 46 subjects and 1572 patients’ cases out of ≈4000 in post-operative pain have been used to design, develop and validate the systems. In the stress management domain, besides the 68 measurement cases, three trainees and one senior clinician also have been involved in order to conduct the experimental work. The result from the evaluation shows that the system reaches a level of performance close to the expert and better than the senior and trainee clinicians. Thus, the proposed CDSS could be used as an expert for a less experienced clinician (i.e. trainee) or as a second option/opinion for an experienced clinician (i.e. senior) to their decision making process in stress management. In post-operative pain treatment, the CDSS retrieves and presents most similar cases (e.g. both rare and regular) with their outcomes to assist physicians. Moreover, an automatic approach is presented in order to identify rare cases and 18% of cases from the whole cases library i.e. 276 out of 1572 are identified as rare cases by the approach. Again, among the rare cases (i.e. 276), around 57.25% of the cases are classified as ‘unusually bad’ i.e. the average pain outcome value is greater or equal to 5 on the NVAS scale 0 to 10. Identification of rear cases is an important part of the PAIN OUT project and can be used to improve the quality of individual pain treatment.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2011
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 113
National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-13166 (URN)978-91-7485-043-7 (ISBN)
Public defence
2011-11-22, Paros, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2011-10-21 Created: 2011-10-21 Last updated: 2018-01-12Bibliographically approved

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Ahmed, Mobyen UddinBegum, ShahinaFunk, PeterXiong, Ning

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