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Chronic pain is prevalent and disproportionately impacts adults with a lower quality of life. Although subjective self-reporting is the “gold standard” for pain assessment, tools are needed to objectively monitor and account for inter-individual differences. This study introduced a novel framework to objectively classify pain intensity levels using physiological signals during Quantitative Sensory Testing sessions. Twenty-four participants participated in the study wearing physiological sensors (blood volume pulse (BVP), galvanic skin response (GSR), electromyography (EMG), respiration rate (RR), skin temperature (ST), and pupillometry). This study employed two analysis plans. Plan 1 utilized a grid search methodology with a 10-fold cross-validation framework to optimize time windows (1–5 s) and machine learning hyperparameters for pain classification tasks. The optimal time windows were identified as 3 s for the pressure session, 2 s for the pinprick session, and 1 s for the cuff session. Analysis Plan 2 implemented a leave-one-out design to evaluate the individual contribution of each sensor modality. By systematically excluding one sensor’s features at a time, the performance of these sensor sets was compared to the full model using Wilcoxon signed-rank tests. BVP emerged as a critical sensor, significantly influencing performance in both pinprick and cuff sessions. Conversely, GSR, RR, and pupillometry demonstrated stimulus-specific sensitivity, significantly contributing to the cuff session but with limited influence in other sessions. EMG and ST showed minimal impact across all sessions, suggesting they are non-critical and suitable for reducing sensor redundancy. These findings advance the design of sensor configurations for personalized pain management. Future research will focus on refining sensor integration and addressing stimulus-specific physiological responses. 

Pain, especially chronic pain, is a complicated and subjective experience, threatening global healthcare as one of the most severe health problems. Traditionally, pain is assessed by Visual Analog Scale to indicate the pain intensity by the patient’s self-report, causing them to become biased by various psychosocial factors. In this study, we performed two distinct labeling methods to assess the pressure pain in Quantitative Sensory Testing and to differentiate healthy controls and chronic low back pain patients: time period labels and percentage timestamp labels. Physiological signals such as blood volume pulse and galvanic skin response were collected. The time period labeling method was to segment via fixed time windows. The percentage timestamp labeling method was to select the timestamp labels based on the percentage of the threshold or the tolerance time. Both methods demonstrate different advantages when visualizing the information of different pain states and different participant groups. 

Pain, a pervasive global health concern, affects a large segment of population worldwide. Accurate pain assessment remains a challenge due to the limitations of conventional self-report scales, which often yield inconsistent results and are susceptible to bias. Recognizing this gap, our study introduces PainAttnNet, a novel deep-learning model designed for precise pain intensity classification using physiological signals. We investigate whether PainAttnNet would outperform existing models in capturing temporal dependencies. The model integrates multiscale convolutional networks, squeeze-and-excitation residual networks, and a transformer encoder block. This integration is pivotal for extracting robust features across multiple time windows, emphasizing feature interdependencies, and enhancing temporal dependency analysis. Evaluation of PainAttnNet on the BioVid heat pain dataset confirm the model’s superior performance over the existing models. The results establish PainAttnNet as a promising tool for automating and refining pain assessments. Our research not only introduces a novel computational approach but also sets the stage for more individualized and accurate pain assessment and management in the future. 

Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing. In response, many synergistic research areas have been coming together to address pain-related issues. This work reviews and analyzes a vast body of pain-related literature using the keyword co-occurrence network (KCN) methodology. In this method, a set of KCNs is constructed by treating keywords as nodes and the co-occurrence of keywords as links between the nodes. Since keywords represent the knowledge components of research articles, analysis of KCNs will reveal the knowledge structure and research trends in the literature. This study extracted and analyzed keywords from 264,560 pain-related research articles indexed in IEEE, PubMed, Engineering Village, and Web of Science published between 2002 and 2021. We observed rapid growth in pain literature in the last two decades: the number of articles has grown nearly threefold, and the number of keywords has grown by a factor of 7. We identified emerging and declining research trends in sensors/methods, biomedical, and treatment tracks. We also extracted the most frequently co-occurring keyword pairs and clusters to help researchers recognize the synergies among different pain-related topics. 

Chronic pain patients lack at-home pain assessment and management tools. The existing chronic-pain mobile applications are either solely relying on self-report pain levels or restricted to formal clinical settings. Our app, abbreviated from an NSF-funded project entitled Novel Computational Methods for Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS), is a multi-dimensional pain app that collects physiological signals to predict objective pain levels and trace daily at-home activities by incorporating a daily check-in section. We conducted a usability test with 33 healthy participants under pain conditions. The results provided initial support for the validity of the signals in predicting internalizing pain levels among the participants. With further development and testing, we believe the COMPASS app system has the potential to be used by both patients and clinicians as an additional tool to better assess and manage pain, especially for mobile healthcare applications. 

Chronic pain is a major cause of disability worldwide. While acute pain may serve as a protective function, chronic pain and the associated changes in neural processing negatively impact function and quality of life. This neural plasticity may include changes to the autonomic nervous system (ANS) potentially detectable as changes in various physiological signals. Our aim is to evaluate differences in the physiological signals reflecting ANS changes, by comparing healthy subjects and patients with chronic low back pain during standardized pain stimuli. We extracted several features from photoplethysmography (PPG), electrodermal activity (EDA), and respiration, both at rest and during a repeated pinprick test. We found significant group differences in some PPG parameters at rest and in response to the repeated pinprick test. Chronic pain patients had consistently higher basal sympathetic activity, as well as a blunted autonomic response when subjected to nociceptive stimuli. 

Automatic pain intensity assessment from physiological signals has become an appealing approach, but it remains a largely unexplored research topic. Most studies have used machine learning approaches built on carefully designed features based on the domain knowledge available in the literature on the time series of physiological signals. However, a deep learning framework can automate the feature engineering step, enabling the model to directly deal with the raw input signals for real-time pain monitoring. We investigated a personalized Bidirectional Long short-term memory Recurrent Neural Networks (BiLSTM RNN), and an ensemble of BiLSTM RNN and Extreme Gradient Boosting Decision Trees (XGB) for four-category pain intensity classification. We recorded Electrodermal Activity (EDA) signals from 29 subjects during the cold pressor test. We decomposed EDA signals into tonic and phasic components and augmented them to original signals. The BiLSTM-XGB model outperformed the BiLSTM classification performance and achieved an average F1-score of 0.81 and an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.93 over four pain states: no pain, low pain, medium pain, and high pain. We also explored a concatenation of the deep-learning feature representations and a set of fourteen knowledge-based features extracted from EDA signals. The XGB model trained on this fused feature set showed better performance than when it was trained on component feature sets individually. This study showed that deep learning could let us go beyond expert knowledge and benefit from the generated deep representations of physiological signals for pain assessment. 

Optimization of pain assessment and treatment is an active area of research in healthcare. The purpose of this research is to create an objective pain intensity estimation system based on multimodal sensing signals through experimental studies. Twenty eight healthy subjects were recruited at Northeastern University. Nine physiological modalities were utilized in this research, namely facial expressions (FE), electroencephalography (EEG), eye movement (EM), skin conductance (SC), and blood volume pulse (BVP), electromyography (EMG), respiration rate (RR), skin temperature (ST), blood pressure (BP). Statistical analysis and machine learning algorithms were deployed to analyze the physiological data. FE, EEG, SC, BVP, and BP proved to be able to detect different pain states from healthy subjects. Multi-modalities proved to be promising in detecting different levels of painful states. A decision-level multi-modal fusion also proved to be efficient and accurate in classifying painful states.