Projects:PainAssessment - Revision history

Ygao at 01:02, 16 November 2013

2013-11-16T01:02:44Z

2012-01-12T16:08:22Z

2010-11-12T18:11:56Z

Publications

2010-11-12T18:10:02Z

Publications

2010-11-12T18:09:49Z

Publications

2009-10-20T19:00:51Z

2009-09-30T00:35:08Z

2009-09-30T00:33:18Z

2009-09-30T00:31:01Z

2009-09-30T00:26:18Z

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-  Back to [[Algorithm:BU|Boston University Algorithms]]
+  Back to [[Algorithm:Stony Brook|Stony Brook University Algorithms]]
 __NOTOC__
 = Agitation and Pain Assessment Using Digital Imaging =

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-  Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
+  Back to [[Algorithm:BU|Boston University Algorithms]]
 __NOTOC__
 = Agitation and Pain Assessment Using Digital Imaging =

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 = Publications =
-B. Gholami, W. M. Haddad, and A. Tannenbaum, “Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging,” IEEE Trans. Biomed. Eng., Vol. 57, 1457-1466.
+B. Gholami, W. M. Haddad, and A. Tannenbaum, “Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging,” IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466.
 B. Gholami, W. M. Haddad, and A. R. Tannenbaum, “Agitation and Pain Assessment Using Digital Imaging,” Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

← Older revision		Revision as of 18:10, 12 November 2010
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	= Publications =		= Publications =
−
−	~~'' In Press''~~

	B. Gholami, W. M. Haddad, and A. Tannenbaum, “Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging,” IEEE Trans. Biomed. Eng., Vol. 57, 1457-1466.		B. Gholami, W. M. Haddad, and A. Tannenbaum, “Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging,” IEEE Trans. Biomed. Eng., Vol. 57, 1457-1466.

@@ Line 126: / Line 126: @@
 '' In Press''
-B. Gholami, W. M. Haddad, and A. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'' (in submission)
+B. Gholami, W. M. Haddad, and A. Tannenbaum, “Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging,” IEEE Trans. Biomed. Eng., Vol. 57, 1457-1466.
 = Key Investigators =
 Georgia Tech: Behnood Gholami, Wassim M. Haddad, and Allen Tannenbaum

← Older revision		Revision as of 00:35, 30 September 2009
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	[[Image:Pain1.JPG\|500px]]		[[Image:Pain1.JPG\|500px]]
	[[Image:pain2.JPG\|500px]]		[[Image:pain2.JPG\|500px]]
		+
		+	== Publication ==
		+
		+	B. Gholami, W. M. Haddad, and A. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'' (in submission)
		+
		+	== Key Investigators ==
		+	Georgia Tech: Behnood Gholami, Wassim M. Haddad, and Allen Tannenbaum

← Older revision		Revision as of 00:33, 30 September 2009
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	linear kernel for each corresponding photograph was rounded		linear kernel for each corresponding photograph was rounded
	off to the nearest multiple of 10.		off to the nearest multiple of 10.
		+
		+	[[Image:Pain1.JPG\|500px]]
		+	[[Image:pain2.JPG\|500px]]

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 blood pressure, and respiratory rate responses should also
 be included in such a system.
 Computer vision techniques can be used to quantify agitation
@@ Line 83: / Line 60: @@
 the leave-one-out method for validation.
-In the preprocessing stage, the faces were standardized for
+The classification accuracy for the SVM algorithm
-their eye position using a similarity transformation. Then, a
+with a linear kernel was 90%. Applying the RVM algorithm
 with a linear kernel to the same data set resulted in an
-almost identical classification accuracy, namely, 91%; while
+almost identical classification accuracy, namely, 91%.
-the number of relevance vectors was reduced to 2. However,
-in 5 out of the 21 subjects considered, the algorithm did
+== Pain Intensity Assessment ==
-not converge. This is due to the fact that, in contrast to the
-SVM algorithm, the RVM algorithm involves a non-convex
+In addition to classification, the RVM algorithm provides
-optimization problem.
+the posterior probability of the membership of a test image to

@@ Line 11: / Line 11: @@
 and taking appropriate action to ameliorate the patient’s
 anxiety and discomfort.
 The fundamental limitations in sedation and pain assessment
 in the ICU stem from subjective assessment criteria,
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 ICU sedation. An automatic pain assessment system can be
 used within a decision support framework which can also
-provide automated sedation and analgesia in the ICU].
+provide automated sedation and analgesia in the ICU.
 In order to achieve closed-loop sedation control in the ICU,
 a quantifiable feedback signal is required that reflects some
@@ Line 27: / Line 28: @@
 assessment algorithm can be a key component in developing
 closed-loop sedation control algorithms for ICU sedation.
 Individuals in pain manifest their condition through "pain
 behavior", which includes facial expressions. Clinicians regard the patient’s facial expression as a valid indicator for
@@ Line 37: / Line 39: @@
 blood pressure, and respiratory rate responses should also
 be included in such a system.
 The current clinical standard in the ICU for assessing
 the level of sedation is an ordinal scoring system, such