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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/100/2248

Title: Application of Crisp and Fuzzy clustering algorithms for identification of hidden patterns from plethysmographic observations on the radial pulse
Authors: KARAMCHANDANI, S
MERCHANT, SN
DESAI, UB
JINDAL, GD
Issue Date: 2010
Publisher: IEEE
Citation: 2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC),3978-3981
Abstract: Radial Pulse forms the most basic and essential physical sign in clinical medicine. The paper proposes the application of crisp and fuzzy clustering algorithms under supervised and unsupervised learning scenarios for identifying non-trivial regularities and relationships of the radial pulse patterns obtained by using the Impedance Plethysmographic technique. The objective of our paper is to unearth the hidden patterns to capture the physiological variabilities from the arterial pulse for clinical analysis, thus providing a very useful tool for disease characterization. A variety of fuzzy algorithms including Gustafson-Kessel (GK) and Gath-Geva (GG) have been intensively tested over a diverse group of subjects and over 4855 data sets. Exhaustive testing over the data set show that about 80 % of the patterns are successfully classified thus providing promising results. A Rank Index of 0.7739 is obtained under supervised learning, which provides an excellent conformity of our process with the results of plethysmographic experts. A correlation of the patterns with the diseases of heart, liver and lungs is judiciously performed.
URI: http://dx.doi.org/10.1109/IEMBS.2010.5627983
http://dspace.library.iitb.ac.in/xmlui/handle/10054/15507
http://hdl.handle.net/100/2248
ISBN: 978-1-4244-4124-2
ISSN: 1557-170X
Appears in Collections:Proceedings papers

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