Quantifying and Characterizing Tonic Thermal Pain Across Subjects From EEG Data Using Random Forest Models

Abstract

Objective Effective pain assessment and management strategies are needed to better manage pain. In addition to self-report, an objective pain assessment system can provide a more complete picture of the neurophysiological basis for pain. In this study, a robust and accurate machine learning approach is developed to quantify tonic thermal pain across healthy subjects into a maximum of ten distinct classes. Methods A random forest model was trained to predict pain scores using time-frequency wavelet representations of independent components obtained from electroencephalography (EEG) data, and the relative importance of each frequency band to pain quantification is assessed. Results The mean classification accuracy for predicting pain on an independent test subject for a range of 1–10 is 89.45%, highest among existing state of the art quantification algorithms for EEG. The gamma band is the most important to both inter-subject and intra-subject classification accuracy. Conclusion The robustness and generalizability of the classifier is demonstrated. Significance Our results demonstrate the potential of this tool to be used clinically to help improve chronic pain treatment, and establish spectral biomarkers for future pain-related studies using EEG.

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

“…The electrical biopotentials generated by the human body are signals commonly used in medical diagnosis. The main biopotentials are electrocardiagram (ECG) [82,85], electromyogram (EMG) [11,35,43,215] and electroencephalogram (EEG) [46,50,[216][217][218][219]. Biopotential sensors are usually composed of the following elements: electrodes, used for the transduction of the ionic signals inside the body to electrical signals; an analog conditioning stage for the amplification of the electrical signal, of very low intensity, at measurable levels avoiding electromagnetic interference (EMI); and an analog-digital conversion stage.…”

“…Since EEG signals are very weak, the application of conductive gels can improve the quality of the signal by reducing the impedance of the electrode-skin contact [46]. EEG has numerous practical advantages, since it is a non-invasive technique, with high temporal resolution, provides relevant clinical information, is low-cost and requires little maintenance [50]. EEG studies have shown the activation of specific regions of the brain as a result of pain stimuli [216,217].…”

“…It should be noted that despite the lack of an objective reference for the evaluation of chronic pain, many studies have tried to induce pain levels in a controlled manner (by heat [23,[41][42][43][44][45][46][47][48][49][50][51][52][53], cold [45,48,52], pressure [44] or electrical stimulation [54]) as a first approximation to a method that allows the evaluation of chronic pain [1]. In contrast to the studies based on research with phasic pain stimuli (short duration), the number of studies based on tonic pain stimuli is growing as an approach to chronic pain [23,[41][42][43][44][45][46][47][48][49][50][51][52][52][53][54][55]. This validation approach represents an objective way for assessing chronic pain, leaving aside all the subjectivity that may be related to the perception of pain by the chronic patient [23,[41][42][43][44][45][46]…”

“…In contrast to the studies based on research with phasic pain stimuli (short duration), the number of studies based on tonic pain stimuli is growing as an approach to chronic pain [23,[41][42][43][44][45][46][47][48][49][50][51][52][52][53][54][55]. This validation approach represents an objective way for assessing chronic pain, leaving aside all the subjectivity that may be related to the perception of pain by the chronic patient [23,[41][42][43][44][45][46][47][48][49][50][51][52][52][53][54][55]. In addition, it becomes the most appropriate option in the case of patients with absence of communication skills, whether verbal, written or tactile (infants, unconscious patients, critically sick, patients with intellectual impairment, advanced dementia, etc.…”

Classification of Pain Event Related Potential for Evaluation of Pain Perception Induced by Electrical Stimulation

“…Table 1 shows a summary of the previous studies on the classification of high pain and low pain caused by different types of pain stimulations, from different EEG analysis. Based on the information in Table 1, it is noticed that although some classification models have been developed, and high accuracy has been achieved using time-frequency representation of EEG signals for multiple classes of cold pain [16][17][18], none of the studies so far have achieved high classification accuracy from feature vector of pain-ERP for multiple pain perception levels. The reason may lie in the lack of the investigation on the component of classification, feature extraction and selection.…”