Agitation and Pain Assessment Using Digital Imaging

Gholami, Behnood; Haddad, Wassim M.; Tannenbaum, Allen R.

Title:

Agitation and Pain Assessment Using Digital Imaging

dc.contributor.author	Gholami, Behnood
dc.contributor.author	Haddad, Wassim M.
dc.contributor.author	Tannenbaum, Allen R.
dc.contributor.corporatename	Georgia Institute of Technology. Dept. of Biomedical Engineering
dc.contributor.corporatename	Emory University. Dept. of Biomedical Engineering
dc.contributor.corporatename	Georgia Institute of Technology. School of Aerospace Engineering
dc.date.accessioned	2010-03-01T20:12:33Z
dc.date.available	2010-03-01T20:12:33Z
dc.date.issued	2009-09
dc.description	©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.	en
dc.description	DOI: 10.1109/IEMBS.2009.5332437
dc.description	Presented at the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, Minnesota, USA, September 2-6, 2009.
dc.description.abstract	Pain assessment in patients who are unable to verbally communicate with medical staff is a challenging problem in patient critical care. The fundamental limitations in sedation and pain assessment in the intensive care unit (ICU) stem from subjective assessment criteria, rather than quantifiable, measurable data for ICU sedation and analgesia. This often results in poor quality and inconsistent treatment of patient agitation and pain from nurse to nurse. Recent advancements in pattern recognition techniques using a relevance vector machine algorithm can assist medical staff in assessing sedation and pain by constantly monitoring the patient and providing the clinician with quantifiable data for ICU sedation. In this paper, we show that the pain intensity assessment given by a computer classifier has a strong correlation with the pain intensity assessed by expert and non-expert human examiners.	en
dc.identifier.citation	Behnood Gholami, Wassim M. Haddad, and Allen R. Tannenbaum, "Agitation and Pain Assessment Using Digital Imaging," 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, 2176-2179.	en
dc.identifier.isbn	978-1-4244-3296-7
dc.identifier.issn	1557-170X
dc.identifier.uri	http://hdl.handle.net/1853/32093
dc.language.iso	en_US	en
dc.publisher	Georgia Institute of Technology	en
dc.publisher.original	Institute of Electrical and Electronics Engineers
dc.subject	Image classification	en
dc.subject	Medical image processing	en
dc.subject	Patient care	en
dc.subject	Patient monitoring	en
dc.subject	Support vector machines	en
dc.title	Agitation and Pain Assessment Using Digital Imaging	en
dc.type	Text
dc.type.genre	Proceedings
dspace.entity.type	Publication
local.contributor.author	Haddad, Wassim M.
local.contributor.corporatename	Wallace H. Coulter Department of Biomedical Engineering
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