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Using Calibration and Fuzzification of Cases for Improved Diagnosis and Treatment of Stress
Mälardalens högskola, Institutionen för datavetenskap och elektronik.ORCID-id: 0000-0002-1212-7637
Mälardalens högskola, Institutionen för datavetenskap och elektronik.ORCID-id: 0000-0003-3802-4721
Mälardalens högskola, Institutionen för datavetenskap och elektronik.ORCID-id: 0000-0002-5562-1424
Mälardalens högskola, Institutionen för datavetenskap och elektronik.ORCID-id: 0000-0001-9857-4317
Vise andre og tillknytning
2006 (engelsk)Inngår i: 8th European Workshop on Case-based Reasoning in the Health Sciences, workshop proceedings, 2006, s. 113-122Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2006. s. 113-122
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-6945OAI: oai:DiVA.org:mdh-6945DiVA, id: diva2:236955
Konferanse
8th European Workshop on Case-based Reasoning in the Health Sciences, Turkey 2006
Tilgjengelig fra: 2009-09-25 Laget: 2009-09-25 Sist oppdatert: 2017-01-25bibliografisk kontrollert
Inngår i avhandling
1. A Personalised Case-Based Stress Diagnosis System Using Physiological Sensor Signals
Åpne denne publikasjonen i ny fane eller vindu >>A Personalised Case-Based Stress Diagnosis System Using Physiological Sensor Signals
2011 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Västerås: Mälardalen University, 2011
Serie
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 103
HSV kategori
Forskningsprogram
datavetenskap
Identifikatorer
urn:nbn:se:mdh:diva-12257 (URN)978-91-7485-018-5 (ISBN)
Disputas
2011-06-20, Pi, Mälardalens högskola, Västerås, 13:35 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2011-05-16 Laget: 2011-05-15 Sist oppdatert: 2018-01-12bibliografisk kontrollert

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