An experimental study of objective pain measurement using pupillary response based on genetic algorithm and artificial neural network

Physiological Sensor Modality Sensitivity Test for Pain Intensity Classification in Quantitative Sensory Testing

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.

Survey on Pain Detection Using Machine Learning Models: Narrative Review

BACKGROUND

Pain, a leading reason people seek medical care, has become a social issue. Automated pain assessment has seen notable advancements over recent decades, addressing a critical need in both clinical and everyday settings.

OBJECTIVE

The objective of this survey was to provide a comprehensive overview of pain and its mechanisms, to explore existing research on automated pain recognition modalities, and to identify key challenges and future directions in this field.

METHODS

A literature review was conducted, analyzing studies focused on various modalities for automated pain recognition. The modalities reviewed include facial expressions, physiological signals, audio cues, and pupil dilation, with a focus on their efficacy and application in pain assessment.

RESULTS

The survey found that each modality offers unique contributions to automated pain recognition, with facial expressions and physiological signals showing particular promise. However, the reliability and accuracy of these modalities vary, often depending on factors such as individual variability and environmental conditions.

CONCLUSIONS

While automated pain recognition has progressed considerably, challenges remain in achieving consistent accuracy across diverse populations and contexts. Future research directions are suggested to address these challenges, enhancing the reliability and applicability of automated pain assessment in clinical practice.

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.

Does past/current pain change pain experience? Comparing self-reports and pupillary responses

Introduction

For decades, a substantial body of research has confirmed the subjective nature of pain. Subjectivity seems to be integrated into the concept of pain but is often confined to self-reported pain. Although it seems likely that past and current pain experiences would interact and influence subjective pain reports, the influence of these factors has not been investigated in the context of physiological pain. The current study focused on exploring the influence of past/current pain on self-reporting and pupillary responses to pain.

Methods

Overall, 47 participants were divided into two groups, a 4°C-10°C group (experiencing major pain first) and a 10°C-4°C group (experiencing minor pain first), and performed cold pressor tasks (CPT) twice for 30 s each. During the two rounds of CPT, participants reported their pain intensity, and their pupillary responses were measured. Subsequently, they reappraised their pain ratings in the first CPT session.

Results

Self-reported pain showed a significant difference (4°C-10°C: p = 0.045; 10°C-4°C: p < 0.001) in the rating of cold pain stimuli in both groups, and this gap was higher in the 10°C-4°C group than in the 4°C-10°C group. In terms of pupillary response, the 4°C-10°C group exhibited a significant difference in pupil diameter, whereas this was marginally significant in the 10°C-4°C group (4°C-10°C: p < 0.001; 10°C-4°C: p = 0.062). There were no significant changes in self-reported pain after reappraisal in either group.

Discussion

The findings of the current study confirmed that subjective and physiological responses to pain can be altered by previous experiences of pain.

Optimal loading method of multi type railway flatcars based on improved genetic algorithm

On the basis of analyzing the complexity of railway flatcar loading optimization problem, according to the characteristics of railway flatcar loading, based on the situation of railway transport loading unit of multiple railway flatcars, this study puts forward the optimal loading optimization method of multimodel railway flatcars based on improved genetic algorithm, constructs the linear programming model of railway flatcar loading optimization problem, and combines with the improved genetic algorithm to solve the problem. The study also analyzes the structural characteristics of the optimal loading materials of multimodel railway flatcars, selects the optimal materials and inputs the relevant data into the computer, and uses MATLAB to program the optimal loading algorithm of multimodel railway flatcars. Through the analysis of the calculation example, the study discusses its scope of application. The experimental results show that the average general utilization rate of the proposed method is 73%, which has higher universality, more effective application, and fully meets the research requirements. It is verified that the proposed method has a statistically significant impact on the optimal loading of multi-type railway flatcars.

Beat-to-beat Estimation of Peripheral Arterial Stiffness from Local PWV for Quantitative Evaluation of Sympathetic Nervous System Activity

Sympathetic nervous system activity (SNSA) can rapidly modulate arterial stiffness, thus making it an important biomarker for SNSA evaluation. Pulse wave velocity (PWV) is a well-known quantitative indicator of arterial stiffness, but its functional responsivity to SNSA has not been elucidated. This paper reports a method to estimate rapid changes in peripheral arterial stiffness induced by SNSA using local PWV (LPWV) and to further quantify SNSA based on the estimated stiffness. LPWV was measured from the artery near the wrist to the artery near the forefinger using a biodegradable piezoelectric sensor and a photoplethysmography sensor in an electrocutaneous stimulus experiment in which pain indicts the SNSA. The relationship between LPWV, simultaneously measured peripheral arterial stiffness index, and self-reported pain intensity was quantified. The stiffness estimated by LPWV alone and the stiffness estimated by LPWV and arterial pressure both approximate the peripheral arterial stiffness index (R² = 0.9775 and 0.9719). Pain intensity can be quantitatively evaluated in a sigmoidal relationship by either the estimated stiffness based on LPWV alone (r = 0.8594) or the estimated stiffness based on LPWV and arterial pressure (r = 0.9738). Our results demonstrated the validity of LPWV in the quantitative evaluation of SNSA and the optionality of blood pressure correction depending on application scenarios. This study advances the understanding of sympathetic innervation of peripheral arteries through the sympathetic responsivity of LPWV and contributes a quantitative biomarker for SNSA evaluation.

Experimental Exploration of Objective Human Pain Assessment Using Multimodal Sensing Signals

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.