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Physiological Sensor Signals Analysis to Represent Cases in a Case-based Diagnostic System
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (IS)ORCID iD: 0000-0002-1212-7637
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (IS)ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. (IS)ORCID iD: 0000-0002-5562-1424
2013 (English)In: Innovations in Knowledge-based Systems in Biomedicine, vol. 250 / [ed] Pham T.D,Jain L.C., Springer, 2013, 1-25 p.Chapter in book (Other academic)
Abstract [en]

Today, medical knowledge is expanding so rapidly that clinicians cannot follow all progress any more. This is one reason for making knowledge- based systems desirable in medicine. Such systems can give a clinician a second opinion and give them access to new experience and knowledge. Recent advances in Artificial Intelligence (AI) offers methods and techniques with the potential of solving tasks previously difficult to solve with computer-based systems in medical domains. This chapter is especially concerned with diagnosis of stress-related dysfunctions using AI methods and techniques. Since there are large individual variations between people when looking at biological sensor signals to diagnose stress, this is a worthy challenge. Stress is an inevitable part of our human life. No one can live without stress. However, long-term exposure to stress may in the worst case cause severe mental and/or physical problems that are often related to different kind of psychosomatic disorders, coronary heart disease etc. So, diagnosis of stress is an important issue for health and well-being. Diagnosis of stress often involves acquisition of biological signals for example finger temperature, electrocardiogram (ECG), electromyography (EMG) signal, skin conductance (SC) signals etc. and is followed by a careful analysis by an expert. However, the number of experts to diagnose stress in psycho-physiological domain is limited. Again, responses to stress are different for different persons. So, interpreting a particular curve and diagnosing stress levels is difficult even for experts in the domain due to large individual variations. It is a highly complex and partly intuitive process which experienced clinicians use when manually inspecting biological sensor signals and classifying a patient. Clinical studies show that the pattern of variation within heart rate i.e., HRV signal and finger temperature can help to determine stress-related disorders. This chapter presents a signal pre-processing and feature extraction approach based on electrocardiogram (ECG) and finger temperature sensor signals. The extracted features are used to formulate cases in a case-based reasoning system to develop a personalized stress diagnosis system. The results obtained from the evaluation show a performance close to an expert in the domain in diagnosing stress.

Place, publisher, year, edition, pages
Springer, 2013. 1-25 p.
Series
Studies in Computational Intelligence, ISSN 1860-949X ; 250
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-17253DOI: 10.1007/978-3-642-33015-5_1Scopus ID: 2-s2.0-84893111384ISBN: 9783642330148 (print)OAI: oai:DiVA.org:mdh-17253DiVA: diva2:579584
Available from: 2012-12-20 Created: 2012-12-20 Last updated: 2017-01-25Bibliographically approved

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