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Multi-Scale Entropy Analysis and Case-Based Reasoning to Classify Physiological Sensor Signals
Mälardalens högskola, Akademin för innovation, design och teknik. (IS)ORCID-id: 0000-0002-1212-7637
Mälardalens högskola, Akademin för innovation, design och teknik. (IS)ORCID-id: 0000-0003-3802-4721
2012 (engelsk)Inngår i: Proceedings of the ICCBR 2012 Workshops / [ed] Luc Lamontagne and Juan A. Recio-García, 2012, s. 129-138Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Sensor signal fusion is becoming increasingly important in many areas including medical diagnosis and classification. Clinicians/experts often do the diagnosis of stress, sleepiness, tiredness etc. based on several physiological sensor signals to achieve better accuracy in classification. This paper presents a case-based reasoning (CBR) system that offers an opportunity to classify healthy and stressed persons based on sensor signal fusion. Several sensor measurements for instance, i.e., heart rate, inter-beat-interval, finger temperature, skin conductance and respiration rate have been combined for the data level fusion using Multivariate Multiscale Entropy Analysis (MMSE) algorithm. This algorithm supports complexity analysis of multivariate biological recordings. Here, MMSE is used to formulate cases in the case-based classification system.

sted, utgiver, år, opplag, sider
2012. s. 129-138
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-17329OAI: oai:DiVA.org:mdh-17329DiVA, id: diva2:579660
Konferanse
The Workshop on Case-Based Reasoning in the Health Sciences, at the International Conference on Case-Based Reasoning, ICCBR-12, Lyon, France, September 4, 2012
Tilgjengelig fra: 2012-12-20 Laget: 2012-12-20 Sist oppdatert: 2017-01-25bibliografisk kontrollert

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