Title:
Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging
Relevance Vector Machine Learning for Neonate Pain Intensity 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. School of Aerospace Engineering | |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Electrical and Computer Engineering | |
| dc.contributor.corporatename | Georgia Institute of Technology. Dept. of Biomedical Engineering | |
| dc.contributor.corporatename | Emory University. Dept. of Biomedical Engineering | |
| dc.date.accessioned | 2010-06-07T20:16:21Z | |
| dc.date.available | 2010-06-07T20:16:21Z | |
| dc.date.issued | 2010-06 | |
| dc.description | ©2010 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_US |
| dc.description | DOI: 10.1109/TBME.2009.2039214 | |
| dc.description.abstract | Pain assessment in patients who are unable to verbally communicate is a challenging problem. The fundamental limitations in pain assessment in neonates stem from subjective assessment criteria, rather than quantifiable andmeasurable data. This often results in poor quality and inconsistent treatment of patient pain management. Recent advancements in pattern recognition techniques using relevance vector machine (RVM) learning techniques can assist medical staff in assessing pain by constantly monitoring the patient and providing the clinician with quantifiable data for pain management. The RVMclassification technique is a Bayesian extension of the support vector machine (SVM) algorithm, which achieves comparable performance to SVMwhile providing posterior probabilities for class memberships and a sparser model. If classes represent “pure” facial expressions (i.e., extreme expressions that an observer can identify with a high degree of confidence), then the posterior probability of the membership of some intermediate facial expression to a class can provide an estimate of the intensity of such an expression. In this paper, we use the RVM classification technique to distinguish pain from nonpain in neonates as well as assess their pain intensity levels. We also correlate our results with the pain intensity assessed by expert and nonexpert human examiners. | en_US |
| dc.identifier.citation | Behnood Gholami, Wassim M. Haddad, and Allen R. Tannenbaum, "Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging," IEEE Transactions on Biomedical Engineering, Vol. 57, No. 6 (June 2010) 1457-1466 | en_US |
| dc.identifier.issn | 0018-9294 | |
| dc.identifier.uri | http://hdl.handle.net/1853/33726 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | Institute of Electrical and Electronics Engineers | |
| dc.subject | Digital imaging | en_US |
| dc.subject | Facial expression recognition | en_US |
| dc.subject | Neonates | en_US |
| dc.subject | Pain assessment | en_US |
| dc.subject | Relevance vector machines | en_US |
| dc.subject | Support vector machines | en_US |
| dc.title | Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging | en_US |
| dc.type | Text | |
| dc.type.genre | Article | |
| dspace.entity.type | Publication | |
| local.contributor.author | Haddad, Wassim M. | |
| local.contributor.corporatename | Wallace H. Coulter Department of Biomedical Engineering | |
| relation.isAuthorOfPublication | 6a6bf54c-ea0c-48c2-b93e-80351c6262d7 | |
| relation.isOrgUnitOfPublication | da59be3c-3d0a-41da-91b9-ebe2ecc83b66 |