EEG-Based Tonic Cold Pain Characterization Using Wavelet Higher Order Spectral Features

In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?

Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.

A systematic review of neurophysiological sensing for the assessment of acute pain

Pain is a complex and personal experience that presents diverse measurement challenges. Different sensing technologies can be used as a surrogate measure of pain to overcome these challenges. The objective of this review is to summarise and synthesise the published literature to: (a) identify relevant non-invasive physiological sensing technologies that can be used for the assessment of human pain, (b) describe the analytical tools used in artificial intelligence (AI) to decode pain data collected from sensing technologies, and (c) describe the main implications in the application of these technologies. A literature search was conducted in July 2022 to query PubMed, Web of Sciences, and Scopus. Papers published between January 2013 and July 2022 are considered. Forty-eight studies are included in this literature review. Two main sensing technologies (neurological and physiological) are identified in the literature. The sensing technologies and their modality (unimodal or multimodal) are presented. The literature provided numerous examples of how different analytical tools in AI have been applied to decode pain. This review identifies different non-invasive sensing technologies, their analytical tools, and the implications for their use. There are significant opportunities to leverage multimodal sensing and deep learning to improve accuracy of pain monitoring systems. This review also identifies the need for analyses and datasets that explore the inclusion of neural and physiological information together. Finally, challenges and opportunities for designing better systems for pain assessment are also presented.

Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain

Objective

We assessed the potential of using EEG to detect cold thermal pain in adolescents with and without chronic musculoskeletal pain.

Methods

Thirty-nine healthy controls (15.2 ± 2.1 years, 18 females) and 121 chronic pain participants (15.0 ± 2.0 years, 100 females, 85 experiencing pain ≥12-months) had 19-channel EEG recorded at rest and throughout a cold-pressor task (CPT). Permutation entropy, directed phase lag index, peak frequency, and binary graph theory features were calculated across 10-second EEG epochs (Healthy: 292 baseline / 273 CPT epochs; Pain: 1039 baseline / 755 CPT epochs). Support vector machine (SVM) and logistic regression models were trained to classify between baseline and CPT conditions separately for control and pain participants.

Results

SVM models significantly distinguished between baseline and CPT conditions in chronic pain (75.2% accuracy, 95% CI: 71.4%–77.1%; p < 0.0001) and control (74.8% accuracy, 95% CI: 66.3%–77.6%; p < 0.0001) participants. Logistic regression models performed similar to the SVM (Pain: 75.8% accuracy, 95% CI: 69.5%–76.6%, p < 0.0001; Controls: 72.0% accuracy, 95% CI: 64.5%–78.5%, p < 0.0001). Permutation entropy features in the theta frequency band were the largest contributor to model accuracy for both groups.

Conclusions

Our results demonstrate that subjective pain experiences can accurately be detected from electrophysiological data, and represent the first step towards the development of a point-of-care system to detect pain in the absence of self-report.

Investigation of the Evolution of Wavelet Higher-Order Dynamics in Atrial Fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with significant morbidity and mortality. Owing to the advances in sensor technology and the emergence of wearable devices that enable daily self-monitoring, ECG signal processing methods for the automatic detection of AF are more pertinent than ever. In this paper, we investigate the use of wavelet higher-order statistics (WHOS) for feature extraction and differentiation between normal sinus rhythm and AF. The proposed approach captures the evolution of the WHOS dynamics and quantifies the changes in the time-varying characteristics of the frequency couplings caused by AF. Results obtained from the statistical analysis of a dataset of 5834 single-lead ECG recordings, reveal 46/50 statistically significant features and provide insight into the complexity of the evolution of the ECG non-linearities during AF.

Learning Spatial-Spectral-Temporal EEG Representations with Deep Attentive-Recurrent-Convolutional Neural Networks for Pain Intensity Assessment

Electroencephalogram (EEG)-based quantitative pain measurement is valuable in the field of clinical pain treatment, providing objective pain intensity assessment especially for nonverbal patients who are unable to self-report. At present, a key challenge in modeling pain events from EEG is to find invariant representations for intra- and inter-subject variations, where current methods based on hand-crafted features cannot provide satisfactory results. Hence, we propose a novel method based on deep learning to learn such invariant representations from multi-channel EEG signals and demonstrate its great advantages in EEG-based pain classification tasks. To begin, instead of using typical EEG analysis techniques that ignore spatial information of EEG, we convert raw EEG signals into a sequence of multi-spectral topography maps (topology-preserving EEG images). Next, inspired by various deep learning techniques applied in neuroimaging domain, a deep Attentive-Recurrent-Convolutional Neural Network (ARCNN) is proposed here to learn spatial-spectral-temporal representations from EEG images. The proposed method aims to jointly preserve the spatial-spectral-temporal structures of EEG, for learning representations with high robustness against intra-subject and inter-subject variations, making it more conducive to multi-class and subject-independent scenarios. Empirical evaluation on 4-level pain intensity assessment within the subject-independent scenario demonstrated significant improvement over baseline and state-of-the-art methods in this field. Our approach applies deep neural networks (DNNs) to pain intensity assessment for the first time and demonstrates its potential advantages in modeling pain events from EEG.

Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data

Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroencephalogram (EEG) data demonstrate promising results. The primary aim of this review was to evaluate the effectiveness of ML algorithms for predicting pain intensity, phenotypes or treatment response from EEG. Electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO and The Cochrane Library were searched. A total of 44 eligible studies were identified, with 22 presenting attempts to predict pain intensity, 15 investigating the prediction of pain phenotypes and seven assessing the prediction of treatment response. A meta-analysis was not considered appropriate for this review due to heterogenos methods and reporting. Consequently, data were narratively synthesized. The results demonstrate that the best performing model of the individual studies allows for the prediction of pain intensity, phenotypes and treatment response with accuracies ranging between 62 to 100%, 57 to 99% and 65 to 95.24%, respectively. The results suggest that ML has the potential to effectively predict pain outcomes, which may eventually be used to assist clinical care. However, inadequate reporting and potential bias reduce confidence in the results. Future research should improve reporting standards and externally validate models to decrease bias, which would increase the feasibility of clinical translation. PERSPECTIVE: This systematic review explores the state-of-the-art machine learning methods for predicting pain intensity, phenotype or treatmentresponse from EEG data. Results suggest that machine learning may demonstrate clinical utility, pending further research and development. Areas for improvement, including standardized processing, reporting and the need for better methodological assessment tools, are discussed.

Differentiation of pain levels by deploying various EEG synchronization features and dynamic ensemble selection mechanism

OBJECTIVE

The target of this study is measuring the pain intensity in an objective manner by analysing the electroencephalogram (EEG) signals. Although this problem has attracted researchers' attention, increasing the resolution of this measurement, by increasing the number of pain states, significantly decreases the accuracy of pain level classification problem.

APPROACH

To overcome this drawback, we adopt state-of-the-art synchronization schemes to measure the linear, nonlinear and generalized synchronization between different EEG channels. 32 subjects executed the Cold Pressor Task (CPT) and experienced five defined levels of pain while recording their EEGs. Due to high number of synchronization features from 34 channels, the most discriminative features were selected using greedy overall relevancy (GOR) method. The selected features are applied to a dynamic ensemble selection system.

MAIN RESULTS

Our experiment provides 85.6% accuracy over the five classes, which significantly outperforms the results of past research. Moreover, we observe that the selected features belong to the channels placed over the ridge of cortex, the area responsible for processing somatic sensation arisen from nociceptive temperature. As expected, we noted that continuing the painful stimulus for minutes engaged regions beyond the sensorimotor cortex, e.g., the prefrontal cortex.

SIGNIFICANCE

We conclude that the amount of synchronization between scalp EEG channels is an informative tool in revealing the pain sensation.

Pain phenotypes classified by machine learning using electroencephalography features

Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an implanted spinal cord stimulator. Analysis of power spectral density, coherence, and phase-amplitude coupling using conventional statistics showed that there were no significant differences between the radiculopathy and control groups after correcting for multiple comparisons. However, analysis of transient spectral events showed that there were differences between these two groups in terms of the number, power, and frequency-span of events in a low gamma band. Finally, we trained a binary support vector machine to classify radiculopathy versus healthy subjects, as well as a 3-way classifier for subjects in the 3 groups. Both classifiers performed significantly better than chance, indicating that EEG features contain relevant information pertaining to sensory states, and may be used to help distinguish between pain states when other clinical signs are inconclusive.

Self-Compassion Demonstrating a Dual Relationship with Pain Dependent on High-Frequency Heart Rate Variability

One previous study indicated the significance of trait self-compassion in psychological well-being and adjustment in people with chronic pain. Higher-frequency heart rate variability (HF-HRV) was found to be closely associated with self-compassion and pain coping. The current study was therefore designed to investigate the relationship between self-compassion and experimental pain as well as the impact of HF-HRV. Sixty healthy participants provided self-reported self-compassion and underwent a cold pain protocol during which HF-HRV was evaluated. Results demonstrated a dual relationship between self-compassion and pain, dependent on the level of HF-HRV during pain exposure. Specifically, self-compassion was associated with lower pain in the condition of higher HF-HRV, while there was an inverse relationship between self-compassion and pain when HF-HRV was lower. Our data indicate the significance of HF-HRV in moderating the association between self-compassion and experimental pain.

Tonic Cold Pain Detection Using Choi–Williams Time-Frequency Distribution Analysis of EEG Signals: A Feasibility Study

Detecting pain based on analyzing electroencephalography (EEG) signals can enhance the ability of caregivers to characterize and manage clinical pain. However, the subjective nature of pain and the nonstationarity of EEG signals increase the difficulty of pain detection using EEG signals analysis. In this work, we present an EEG-based pain detection approach that analyzes the EEG signals using a quadratic time-frequency distribution, namely the Choi–Williams distribution (CWD). The use of the CWD enables construction of a time-frequency representation (TFR) of the EEG signals to characterize the time-varying spectral components of the EEG signals. The TFR of the EEG signals is analyzed to extract 12 time-frequency features for pain detection. These features are used to train a support vector machine classifier to distinguish between EEG signals that are associated with the no-pain and pain classes. To evaluate the performance of our proposed approach, we have recorded EEG signals for 24 healthy subjects under tonic cold pain stimulus. Moreover, we have developed two performance evaluation procedures—channel- and feature-based evaluation procedures—to study the effect of the utilized EEG channels and time-frequency features on the accuracy of pain detection. The experimental results show that our proposed approach achieved an average classification accuracy of 89.24% in distinguishing between the no-pain and pain classes. In addition, the classification performance achieved using our proposed approach outperforms the classification results reported in several existing EEG-based pain detection approaches.

Continuous wavelet transform and higher-order spectrum: combinatory potentialities in breath sound analysis and electroencephalogram-based pain characterization

The combination of the continuous wavelet transform (CWT) with a higher-order spectrum (HOS) merges two worlds into one that conveys information regarding the non-stationarity, non-Gaussianity and nonlinearity of the systems and/or signals under examination. In the current work, the third-order spectrum (TOS), which is used to detect the nonlinearity and deviation from Gaussianity of two types of biomedical signals, that is, wheezes and electroencephalogram (EEG), is combined with the CWT to offer a time-scale representation of the examined signals. As a result, a CWT/TOS field is formed and a time axis is introduced, creating a time-bifrequency domain, which provides a new means for wheeze nonlinear analysis and dynamic EEG-based pain characterization. A detailed description and examples are provided and discussed to showcase the combinatory potential of CWT/TOS in the field of advanced signal processing.This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.

Models for Nociception Stimulation and Memory Effects in Awake and Aware Healthy Individuals

OBJECTIVE

This paper introduces a primer in the health care practice, namely a mathematical model and methodology for detecting and analysing nociceptor stimulation followed by related tissue memory effects.

METHODS

Noninvasive nociceptor stimulus protocol and prototype device for measuring bioimpedance is provided. Various time instants, sensor location, and stimulus train have been analysed.

RESULTS

The method and model indicate that nociceptor stimulation perceived as pain in awake healthy volunteers is noninvasively detected. The existence of a memory effect is proven from data. Sensor location had minimal effect on detection level, while day-to-day variability was observed without being significant.

CONCLUSION

Following the experimental study, the model enables a comprehensive management of chronic pain patients, and possibly other analgesia, or pain related regulatory loops.

SIGNIFICANCE

A device and methodology for noninvasive for detecting nociception stimulation have been developed. The proposed method and models have been validated on healthy volunteers.

Research: Use of Dry Electroencephalogram and Support Vector for Objective Pain Assessment

The reliability of normal gel-based electrode electroencephalogram (EEG) for measuring pain has been validated. To date, however, few documented trials have used dry EEG for pain quantification. The primary goal of this study was to objectively quantify pain using dry EEG in conjunction with a support vector machine (SVM). SVMs have been proven accurate for classifying pain intensity. The authors believe that EEG combined with an SVM could increase the statistical power of pain assessment. Currently, clinicians primarily rely on verbal (i.e., subjective) reports for assessing pain; therefore, the research described here could offer a method to objectively monitor pain, eliminate observer error, and individualize treatment.

Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features

The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12-lead ECG are evaluated. Extreme learning machine and support vector machine (SVM) classifiers are used to evaluate the performance of CWSB features. Experimental results show that the proposed CWSB features of 12-lead ECG and the SVM classifier are successful for classification of various heart pathologies. The individual accuracy values for MI, HMD and BBB classes are obtained as 98.37, 97.39 and 96.40%, respectively, using SVM classifier and radial basis function kernel function. A comparison has also been made with existing 12-lead ECG-based cardiac disease detection techniques.