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Intelligent Signal Analysis Using Case-Based Reasoning for Decision Support in Stress Management

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Computational Intelligence in Healthcare 4

Part of the book series: Studies in Computational Intelligence ((SCI,volume 309))

Abstract

Modern daily life triggers stress for many people in different everyday situations. Consequently many people live with increased stress levels under long durations. It is recognized that increased exposure to stress may cause serious health problems if undiagnosed and untreated. One of the physiological parameters for quantifying stress levels is finger temperature, which helps clinicians with the diagnosis and treatment of stress. However, in practice, the complex and varying nature of signals often makes it difficult and tedious for a clinician and particularly less experienced clinicians to understand, interpret and analyze complex and lengthy sequential measurements. There are very few experts who are able to diagnose and predict stress-related problems; hence a system that can help clinicians in diagnosing stress is important. In order to provide decision support in stress management using this complex data source, case-based reasoning (CBR) is applied as the main methodology to facilitate experience reuse and decision justification. Feature extraction methods which aim to yield compact representation of original signals into case indexes are investigated. A fuzzy technique is also incorporated into the system to perform matching between the features derived from signals to better accommodate vagueness and uncertainty inherently existing in clinicians reasoning as well as imprecision in case description. The validation of the approach is based on close collaboration with experts and measurements from twenty four people used as a reference. The system formulates a new problem case with 17 extracted features from finger temperature signal data. Every case contains fifteen minutes of data from 1800 samples. Thirty nine time series from 24 people have been used to evaluate the approach (matching algorithms) in which the system shows a level of performance close to an experienced expert. Consequently, the system can be used as an expert for a less experienced clinician or as a second option for an experienced clinician to supplement their decision making task in stress management.

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Authors and Affiliations

  1. School of Innovation, Design and Engineering, Mälardalen University, PO Box 883, SE-721 23, Västerås, Sweden

    Shahina Begum, Mobyen Uddin Ahmed, Peter Funk & Ning Xiong

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  1. Shahina Begum
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  2. Mobyen Uddin Ahmed
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  3. Peter Funk
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  4. Ning Xiong
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Editors and Affiliations

  1. Institute of Technology/Computing and Software Systems, University ofWashington, Tacoma, 1900 Commerce Street, Campus Box 358426, 98402, Tacoma, WA, USA

    Isabelle Bichindaritz

  2. Flinders Medical Centre, Adelaide, South Australia, Australia

    Sachin Vaidya

  3. Royal Adelaide Hospital, Adelaide, South Australia, Australia

    Ashlesha Jain

  4. School of Electrical and Information Engineering, University of South Australia, SA 5095, South Australia, Australia

    Lakhmi C. Jain

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Begum, S., Ahmed, M.U., Funk, P., Xiong, N. (2010). Intelligent Signal Analysis Using Case-Based Reasoning for Decision Support in Stress Management. In: Bichindaritz, I., Vaidya, S., Jain, A., Jain, L.C. (eds) Computational Intelligence in Healthcare 4. Studies in Computational Intelligence, vol 309. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14464-6_8

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  • DOI: https://doi.org/10.1007/978-3-642-14464-6_8

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