Pain 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 suering 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 classication 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 classication of fNIRS signals according to dierent 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 identication 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 nox- ious 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 dierent distinctive characteris- tics in each domain. The features were evaluated by ve 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 classiers 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 method- ology. The stimulation paradigm showed a haemodynamic response in the primary somatosensory cortex (S1) and opposite to the hand of application,which was con- sistent with similar published literature. Most of the obtained features showed a nor- mal 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 max- imum amplitude) and F7 (Fourier coecient) were among the top ranked features. The classication models tested the signicance of each ranking to classify the data in four types of noxious stimuli,these results showed that the best classiers 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 classier showed an accuracy of 89:44%. Therefore,this subset of 13 features was identied as probable biomarker of pain using fNIRS. The major contributions of this PhD research can be summarized in ve items: a) presenting the classication 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 dierent domain representations,d) presenting a comparison between feature selection methods and learning models,to obtain the best representation of the pain database, e) nding a subset of features to be dened 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 benet 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||1 Jan 2018|