AbstractPain is a subjective experience, and no objective clinically available diagnosis test exists
to assess it. In clinical practice, the most accepted and valid method is self-reports, this
method relies on a patient’s ability to communicate a self-assessment of pain. However,
the absence of verbal (or writing) communication in some patients (also referred as
non-verbal) is an obstacle to the evaluation of pain, which may derived in risk to death
and under- or over-treatment. Patients with impaired communication, unconscious
patients, infants, stroke survivors, the critically ill, and persons suffering from advance
dementia are examples of vulnerable individuals who are unable to communicate.
Therefore, the need for a reliable and objective pain assessment to assist medical
practitioners in the diagnosis and management of pain.
Attempts to use neuroimaging and computational methods to identify pain in healthy
humans have shown potential to aid the assessment of pain. Previous studies using
functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG) and
Near-Infrared Spectroscopy (NIRS), to predict and identify patterns of noxious signals
(e.g., heat, cold) from non-painful stimuli have proved the use of brain signals to
retrieve pain information. These results show that pain recognition and classification
is plausible using neuroimaging methods, also the results from these studies advocate
for the use of machine learning techniques to predict human pain.
The objective of this PhD research is to investigate the use of functional NIRS (fNIRS)
and machine learning techniques to objectively estimate the pain status of non-verbal
patients. This PhD research also aims to expand previous studies by exploring the
classification of fNIRS signals according to different types of thermal (cold and hot)
pain and corresponding pain intensity (low and high) using machine learning models.
In addition, another purpose of this PhD research is the identification of a potential
biomarker of pain based on fNIRS.
Based on the literature reviewed, the methodology created in this PhD thesis consisted
of data collection, data analysis and interpretation. This included designing a noxious
stimulation procedure using the thermal test following the Quantitative Sensory
Testing (QST) method to obtain the pain threshold and pain tolerance in eighteen
(n = 18) volunteers while recording their haemodynamic activity using an fNIRS system.
This database was labelled according to their corresponding type of stimuli:
low-cold, low-heat, high-cold, and high-heat. Then, from the fNIRS signals a total
of 69 features were established, 9 features in time, 23 features in frequency, and 37
features in wavelet domain; these features represent different distinctive characteristics
in each domain. The features were evaluated by five feature selection methods
and ranked based on relevance to the task and redundancy according to: Information
Gain (IG), Joint Mutual Information (JMI), Student’s t-test (t-test), Chi-squared
(X2), and Fast Correlation Based Filter (FCBF). The feature rankings were used to
train and test three classifiers separately, the Linear Discriminant Analysis (LDA),
K-Nearest Neighbour (K-NN) (with K = 1, 3, 5, 7, 9), and Support Vector Machines
(SVM) (linear, Gaussian, and polynomial kernels).
The results of the this PhD research proved the adequacy of the designed methodology.
The stimulation paradigm showed a haemodynamic response in the primary
somatosensory cortex (S1) and opposite to the hand of application, which was
consistent with similar published literature. Most of the obtained features showed
a normal distribution, while other presented a slightly positive skewed distribution;
some features also showed high correlation among them, suggesting redundancy.
Feature selection methods ranked the obtained features, showing that timemax (time to
maximum amplitude) and F7 (Fourier coefficient) were among the top ranked features.
The classification models tested the significance of each ranking to classify the data
in four types of noxious stimuli, these results showed that the best classifiers were 1-
NN, the Gaussian SVM and the polynomial SVM. The highest accuracy (94.16%) was
exhibited by the Gaussian SVM using the top 25 features ranked by (JMI), however,
using only 13 features this classifier showed an accuracy of 89.44%. Therefore, this
subset of 13 features was identified as probable biomarker of pain using fNIRS.
The major contributions of this PhD research can be summarized in five items: a)
presenting the classification of four types of noxious stimuli, b) showing that pain
threshold and pain tolerance from the QST could be used to obtain pain information
from non-verbal patients, c) increasing generalization by taking measurements from
different domain representations, d) presenting a comparison between feature selection
methods and learning models, to obtain the best representation of the pain database,
e) finding a subset of features to be defined as potential biomarker of pain using fNIRS
This PhD research demonstrates the application of fNIRS in the development of a
physiologically-based diagnosis of human pain that would benefit vulnerable patients
who cannot self-report pain, and presents a set of features as possible biomarker of
pain as measured by fNIRS. However, there is still further research need to develop a
bedside monitor for the diagnosis of pain in clinical settings.
|Date of Award||2018|
|Supervisor||Xu Huang (Supervisor) & Dat Tran (Supervisor)|