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= Agitation and Pain Assessment Using Digital Imaging =

= Description =

Pain assessment in patients who are unable to verbally
communicate with the medical staff is a challenging problem
in patient critical care. This problem is most prominently
encountered in sedated patients in the intensive care unit
(ICU) recovering from trauma and major surgery, as well
as infant patients and patients with brain injuries.
Current practice in the ICU requires the nursing staff in
assessing the pain and agitation experienced by the patient,
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,
rather than quantifiable, measurable data for ICU sedation.
This often results in poor quality and inconsistent treatment
of patient agitation from nurse to nurse. Recent advances
in computer vision techniques can assist the medical staff
in assessing sedation and pain by constantly monitoring the
patient and providing the clinician with quantifiable data for
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.
In order to achieve closed-loop sedation control in the ICU,
a quantifiable feedback signal is required that reflects some
measure of the patient’s agitation. A non-subjective agitation
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
pain and pain intensity. Hence, correct interpretation of
the facial expressions of the patient and its correlation with
pain is a fundamental step in designing an automated pain
assessment system. Of course, other pain behaviors including
head movement and the movement of other body parts, along
with physiological indicators of pain, such as heart rate,
blood pressure, and respiratory rate responses should also
be included in such a system.

Computer vision techniques can be used to quantify agitation
in sedated ICU patients. In particular, such techniques
can be used to develop objective agitation measurements
from patient motion. In the case of paraplegic patients, whole
body movement is not available, and hence, monitoring the
whole body motion is not a viable solution. In this case,
measuring head motion and facial grimacing for quantifying
patient agitation in critical care can be a useful alternative.

== Pain Recognition using Sparse Kernel Machines ==

Support Vector Machines (SVM) and Relevance Vector Machines (RVM) were
used to identify the facial expressions corresponding to pain.
A total of 21 subjects from the infant COPE database were
selected such that for each subject at least one photograph
corresponded to pain and one to non-pain. The total number
of photographs available for each subject ranged between 5
to 12, with a total of 181 photographs considered. We applied
the leave-one-out method for validation.

The classification accuracy for the SVM algorithm
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%. 

== Pain Intensity Assessment ==

In addition to classification, the RVM algorithm provides
the posterior probability of the membership of a test image to
a class. As discussed earlier, using a Bayesian interpretation
of probability, the probability of an event can be interpreted
as the degree of the uncertainty associated with such an
event. This uncertainty can be used to estimate pain intensity.

In particular, if a classifier is trained with a series of facial
images corresponding to pain and non-pain, then there is
some uncertainty for associating the facial image of a person
experiencing moderate pain to the pain class. The efficacy
of such an interpretation of the posterior probability was
validated by comparing the algorithm’s pain assessment
with that assessed by several experts (intensivists) and nonexperts.

In order to compare the pain intensity assessment given by
the RVM algorithm with human assessment, we compared
the subjective measurement of the pain intensity assessed
by expert and non-expert examiners with the uncertainty in
the pain class membership (posterior probability) given by
the RVM algorithm. We chose 5 random infants from the
COPE database, and for each subject two photographs of
the face corresponding to the non-pain and pain conditions
were selected. In the selection process, photographs were
selected where the infant’s facial expression truly reflected
the pain condition—calm for non-pain and distressed for
pain—and a score of 0 and 100, respectively, was assigned
to these photographs to give the human examiner a fair prior
knowledge for the assessment of the pain intensity.

Ten data examiners were asked to provide a score ranging
from 0 to 100 for each new photograph of the same subject,
using a multiple of 10 for the scores. Five examiners with no
medical expertise and five examiners with medical expertise
were selected for this assessment. The medical experts were
members of the clinical staff at the intensive care unit of
the Northeast Georgia Medical Center, Gainesville, GA,
consisting of one medical doctor, one nurse practitioner, and
three nurses. They were asked to assess the pain for a series
of random photographs of the same subject, with the criterion
that a score above 50 corresponds to pain, and with the higher
score corresponding to a higher pain intensity. Analogously,
a score below 50 corresponds to non-pain, with the higher
score corresponding to a higher level of discomfort. The
posterior probability given by the RVM algorithm with a
linear kernel for each corresponding photograph was rounded
off to the nearest multiple of 10.

[[Image:Pain1.JPG|500px]]
[[Image:pain2.JPG|500px]]

= 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, 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).

= Key Investigators =

Georgia Tech: Behnood Gholami, Wassim M. Haddad, and Allen Tannenbaum