mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Intelligent Signal Analysis Using Case-Based Reasoning for Decision Support in Stress Management
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0002-1212-7637
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0001-9857-4317
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0002-5562-1424
2010 (English)In: Computational Intelligence in Healthcare 4: Advanced Methodologies / [ed] Isabelle Bichindaritz et. al., Springer Berlin/Heidelberg, 2010, p. 159-189Chapter in book (Other academic)
Abstract [en]

The complexity of modern lifestyle and society brings many advantages but also causes increased levels of stress for many people. It is known that increased exposure to stress may cause serious health problems if undiagnosed and untreated and a report from the Swedish government estimates that 1/3 of all long term sick leave is stress related. One of the physiological parameters for quantifying stress levels is the finger temperature that helps the clinician in diagnosis and treatment of stress. However, in practice, the complex and varying nature of signals makes it difficult and tedious to interpret and analyze the lengthy sequential measurements. A computer based system diagnosing stress would be valuable both for clinicians and for treatment. Since stress diagnosis has a week domain theory and there are large individual variations, Case-Based Reasoning is proposed as the main methodology. Feature extraction methods abstracting the original signals without losing key features are investigated. A fuzzy technique is also incorporated into the system to perform matching between the features derived from signals to better accommodate vagueness, uncertainty often present in clinical reasoning Validation of the approach is based on close collaboration with experts and measurements from twenty four persons. The system formulates a new problem case with 17 extracted features from the fifteen minutes (1800 samples) of biomedical sensor data. Thirty nine time series from twenty four persons have been used to evaluate the approach (matching algorithms) in which the system shows a level of performance close to an experienced expert. The system can be used as an expert for a less experienced clinician, as a second option for an experienced clinician or for treatment in home environment.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2010. p. 159-189
Series
Studies in Computational Intelligence, ISSN 1860-949X ; 309
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-9032DOI: 10.1007/978-3-642-14464-6-8Scopus ID: 2-s2.0-78049296147ISBN: 978-3-642-14463-9 (print)OAI: oai:DiVA.org:mdh-9032DiVA, id: diva2:301603
Available from: 2010-03-03 Created: 2010-03-03 Last updated: 2017-01-25Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Begum, ShahinaAhmed, Mobyen UddinXiong, NingFunk, Peter

Search in DiVA

By author/editor
Begum, ShahinaAhmed, Mobyen UddinXiong, NingFunk, Peter
By organisation
School of Innovation, Design and Engineering
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 58 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf