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A Personalised Case-Based Stress Diagnosis System Using Physiological Sensor Signals
Mälardalen University, School of Innovation, Design and Engineering. (Artificial Intelligence)ORCID iD: 0000-0002-1212-7637
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Stress is an increasing problem in our present world. It is recognised that increased exposure to stress may cause serious health problems if undiagnosed and untreated. In stress medicine, clinicians’ measure blood pressure, Electrocardiogram (ECG), finger temperature and respiration rate etc. during a number of exercises to diagnose stress-related disorders. However, in practice, it is difficult and tedious for a clinician to understand, interpret and analyze complex, lengthy sequential sensor signals. There are few experts who are able to diagnose and predict stress-related problems. Therefore, a system that can help clinicians in diagnosing stress is important.

This research work has investigated Artificial Intelligence techniques for developing an intelligent, integrated sensor system to establish diagnosis and treatment plans in the psychophysiological domain. This research uses physiological parameters i.e., finger temperature (FT) and heart rate variability (HRV) for quantifying stress levels.  Large individual variations in physiological parameters are one reason why case-based reasoning is applied as a core technique to facilitate experience reuse by retrieving previous similar cases. Feature extraction methods to represent important features of original signals for case indexing are investigated. Furthermore, fuzzy techniques are also employed and incorporated into the case-based reasoning system to handle vagueness and uncertainty inherently existing in clinicians’ reasoning.

The evaluation of the approach is based on close collaboration with experts and measurements of FT and HRV from ECG data. The approach has been evaluated with clinicians and trial measurements on subjects (24+46 persons). An expert has ranked and estimated the similarity for all the subjects during classification. The result shows that the system reaches a level of performance close to an expert in both the cases. The proposed system could be used as an expert for a less experienced clinician or as a second opinion for an experienced clinician to supplement their decision making tasks in stress diagnosis.

Place, publisher, year, edition, pages
Västerås: Mälardalen University , 2011.
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 103
National Category
Computer Science
Research subject
Computer Science
URN: urn:nbn:se:mdh:diva-12257ISBN: 978-91-7485-018-5 (print)OAI: diva2:417071
Public defence
2011-06-20, Pi, Mälardalens högskola, Västerås, 13:35 (English)
Available from: 2011-05-16 Created: 2011-05-15 Last updated: 2013-12-03Bibliographically approved
List of papers
1. Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments
Open this publication in new window or tab >>Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments
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2011 (English)In: IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews, ISSN 1094-6977, Vol. 41, no 4, 421-434 p.Article in journal (Refereed) Published
Abstract [en]

The Health Sciences are, nowadays, one of the major application areas for case-based reasoning (CBR). The paper presents a survey of recent medical CBR systems based on a literature review and an e-mail questionnaire sent to the corresponding authors of the papers where these systems are presented. Some clear trends have been identified, such as multipurpose systems: more than half of the current medical CBR systems address more than one task. Research on CBR in the area is growing, but most of the systems are still prototypes and not available on the market as commercial products. However, many of the projects/systems are intended to be commercialized.

urn:nbn:se:mdh:diva-10845 (URN)10.1109/TSMCC.2010.2071862 (DOI)000291823300001 ()2-s2.0-79959617723 (ScopusID)
Available from: 2010-11-10 Created: 2010-11-10 Last updated: 2017-01-25Bibliographically approved
2. Using Calibration and Fuzzification of Cases for Improved Diagnosis and Treatment of Stress
Open this publication in new window or tab >>Using Calibration and Fuzzification of Cases for Improved Diagnosis and Treatment of Stress
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2006 (English)In: 8th European Workshop on Case-based Reasoning in the Health Sciences, workshop proceedings, 2006, 113-122 p.Conference paper (Refereed)
Abstract [en]

In the medical literature there are a number of physiological reactions related to cognitive activities. Psychosocial and psychophysiological stress is such activities reflected in physiological reactions. Stress related symptoms are highly individual, but decreased hands temperature is the common for most individuals. A clinician learns with experience how to interpret the different symptoms but there is no adaptive diagnostic system for diagnosing stress. Decision support systems (DSS) diagnosing stress would be valuable both for junior clinicians and as second opinion for experts. Due to the large individual variations and no general set of rules, DSS are difficult to build for this task. The proposed solution combines a calibration phase with case-based reason¬ing approach and fuzzification of cases. During the calibration phase a number of individual parameters and case specific fuzzy membership functions are es-tablishes. This case-based approach may help the clinician to make a diagnosis, classification and treatment plan. The case may also be used to follow the treat-ment progress. This may be done using the proposed system. Initial tests show promising results. The individual cases including calibration and fuzzy mem-bership functions may also be used in an autonomous system in home environ-ment for treatment programs for individuals often under high stress.

National Category
Computer Systems
urn:nbn:se:mdh:diva-6945 (URN)
8th European Workshop on Case-based Reasoning in the Health Sciences, Turkey 2006
Available from: 2009-09-25 Created: 2009-09-25 Last updated: 2017-01-25Bibliographically approved
3. Sensor Signal Processing to Extract Features from Finger Temperature in a Case-Based Stress Classification Scheme
Open this publication in new window or tab >>Sensor Signal Processing to Extract Features from Finger Temperature in a Case-Based Stress Classification Scheme
2009 (English)In: WISP 2009: 6TH IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING, PROCEEDINGS, 2009, 193-198 p.Conference paper (Refereed)
Abstract [en]

One of the physiological parameters for quantifying stress levels is the finger temperature that helps the clinician in diagnosis and treatment of stress. However, this pattern of the finger temperature sensor signal is so individual and in practice, it is difficult and tedious even for experienced clinicians to interpret and analyze the signal to classify individual stress levels. So there is an inherent need to develop methods or techniques providing computational solution to utilize this sensor signal in a computer-based system. This paper presents a feature extraction approach based on finger temperature sensor signal. The extracted features are then used to formulate cases in a case-based reasoning system to classify individual sensitivity to stress. The evaluation result shows an encouraging performance to apply the approach in feature extraction from slowly changing sensor signals such as finger temperature signal. 

National Category
Engineering and Technology
urn:nbn:se:mdh:diva-8984 (URN)10.1109/WISP.2009.5286562 (DOI)000276341800034 ()2-s2.0-71249105449 (ScopusID)978-1-4244-5058-9 (ISBN)
6th IEEE International Symposium on Intelligent Signal Processing Location: Budapest, HUNGARY Date: AUG 26-28, 2009
Available from: 2010-03-03 Created: 2010-03-03 Last updated: 2013-12-03Bibliographically approved

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