Differential Effects of Thermal Stimuli in Eliciting Temporal Contrast Enhancement: A Psychophysical Study.

Offset analgesia (OA) is observed when pain relief is disproportional to the reduction of noxious input and is based on temporal contrast enhancement (TCE). This phenomenon is believed to reflect the function of the inhibitory pain modulatory system. However, the mechanisms contributing to this phenomenon remain poorly understood, with previous research focusing primarily on painful stimuli and not generalizing to nonpainful stimuli. Therefore, the aim of this study was to investigate whether TCE can be induced by noxious as well as innocuous heat and cold stimuli. Asymptomatic subjects (n = 50) were recruited to participate in 2 consecutive experiments. In the first pilot study (n = 17), the parameters of noxious and innocuous heat and cold stimuli were investigated in order to implement them in the main study. In the second (main) experiment, subjects (n = 33) participated in TCE paradigms consisting of 4 different modalities, including noxious heat (NH), innocuous heat (IH), noxious cold (NC), and innocuous cold (IC). The intensity of the sensations of each thermal modality was assessed using an electronic visual analog scale. TCE was confirmed for NH (P < .001), NC (P = .034), and IC (P = .002). Conversely, TCE could not be shown for IH (P = 1.00). No significant correlation between TCE modalities was found (r < .3, P > .05). The results suggest that TCE can be induced by both painful and nonpainful thermal stimulation but not by innocuous warm temperature. The exact underlying mechanisms need to be clarified. However, among other potential mechanisms, this may be explained by a thermo-specific activation of C-fiber afferents by IH and of A-fiber afferents by IC, suggesting the involvement of A-fibers rather than C-fibers in TCE. More research is needed to confirm a peripheral influence. PERSPECTIVE: This psychophysical study presents the observation of temporal contrast enhancement during NH, NC, and innocuous cold stimuli but not during stimulation with innocuous warm temperatures in healthy volunteers. A better understanding of endogenous pain modulation mechanisms might be helpful in explaining the underlying aspects of pain disorders.

Objective Measurement of Subjective Pain Perception with Autonomic Body Reactions in Healthy Subjects and Chronic Back Pain Patients: An Experimental Heat Pain Study.

Multiple attempts to quantify pain objectively using single measures of physiological body responses have been performed in the past, but the variability across participants reduces the usefulness of such methods. Therefore, this study aims to evaluate whether combining multiple autonomic parameters is more appropriate to quantify the perceived pain intensity of healthy subjects (HSs) and chronic back pain patients (CBPPs) during experimental heat pain stimulation. HS and CBPP received different heat pain stimuli adjusted for individual pain tolerance via a CE-certified thermode. Different sensors measured physiological responses. Machine learning models were trained to evaluate performance in distinguishing pain levels and identify key sensors and features for the classification task. The results show that distinguishing between no and severe pain is significantly easier than discriminating lower pain levels. Electrodermal activity is the best marker for distinguishing between low and high pain levels. However, recursive feature elimination showed that an optimal subset of features for all modalities includes characteristics retrieved from several modalities. Moreover, the study's findings indicate that differences in physiological responses to pain in HS and CBPP remain small.

Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition.

Artificial intelligence and especially deep learning methods have achieved outstanding results for various applications in the past few years. Pain recognition is one of them, as various models have been proposed to replace the previous gold standard with an automated and objective assessment. While the accuracy of such models could be increased incrementally, the understandability and transparency of these systems have not been the main focus of the research community thus far. Thus, in this work, several outcomes and insights of explainable artificial intelligence applied to the electrodermal activity sensor data of the PainMonit and BioVid Heat Pain Database are presented. For this purpose, the importance of hand-crafted features is evaluated using recursive feature elimination based on impurity scores in Random Forest (RF) models. Additionally, Gradient-weighted class activation mapping is applied to highlight the most impactful features learned by deep learning models. Our studies highlight the following insights: (1) Very simple hand-crafted features can yield comparative performances to deep learning models for pain recognition, especially when properly selected with recursive feature elimination. Thus, the use of complex neural networks should be questioned in pain recognition, especially considering their computational costs; and (2) both traditional feature engineering and deep feature learning approaches rely on simple characteristics of the input time-series data to make their decision in the context of automated pain recognition.

Psychological mechanisms of offset analgesia: The effect of expectancy manipulation.

A frequently used paradigm to quantify endogenous pain modulation is offset analgesia, which is defined as a disproportionate large reduction in pain following a small decrease in a heat stimulus. The aim of this study was to determine whether suggestion influences the magnitude of offset analgesia in healthy participants. A total of 97 participants were randomized into three groups (hypoalgesic group, hyperalgesic group, control group). All participants received four heat stimuli (two constant trials and two offset trials) to the ventral, non-dominant forearm while they were asked to rate their perceived pain using a computerized visual analogue scale. In addition, electrodermal activity was measured during each heat stimulus. Participants in both intervention groups were given a visual and verbal suggestion about the expected pain response in an hypoalgesic and hyperalgesic manner. The control group received no suggestion. In all groups, significant offset analgesia was provoked, indicated by reduced pain ratings (p < 0.001) and enhanced electrodermal activity level (p < 0.01). A significant group difference in the magnitude of offset analgesia was found between the three groups (F[2,94] = 4.81, p < 0.05). Participants in the hyperalgesic group perceived significantly more pain than the hypoalgesic group (p = 0.031) and the control group (p < 0.05). However, the electrodermal activity data did not replicate this trend (p > 0.05). The results of this study indicate that suggestion can be effective to reduce but not increase endogenous pain modulation quantified by offset analgesia in healthy participants.

SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors.

The perception of hunger and satiety is of great importance to maintaining a healthy body weight and avoiding chronic diseases such as obesity, underweight, or deficiency syndromes due to malnutrition. There are a number of disease patterns, characterized by a chronic loss of this perception. To our best knowledge, hunger and satiety cannot be classified using non-invasive measurements. Aiming to develop an objective classification system, this paper presents a multimodal sensory system using associated signal processing and pattern recognition methods for hunger and satiety detection based on non-invasive monitoring. We used an Empatica E4 smartwatch, a RespiBan wearable device, and JINS MEME smart glasses to capture physiological signals from five healthy normal weight subjects inactively sitting on a chair in a state of hunger and satiety. After pre-processing the signals, we compared different feature extraction approaches, either based on manual feature engineering or deep feature learning. Comparative experiments were carried out to determine the most appropriate sensor channel, device, and classifier to reliably discriminate between hunger and satiety states. Our experiments showed that the most discriminative features come from three specific sensor modalities: Electrodermal Activity (EDA), infrared Thermopile (Tmp), and Blood Volume Pulse (BVP).

To Calibrate or not to Calibrate? A Methodological Dilemma in Experimental Pain Research.

To calibrate or not to calibrate? This question is raised by almost everyone designing an experimental pain study with supra-threshold stimulation. The dilemma is whether to individualize stimulus intensity to the pain threshold / supra-threshold pain level of each participant or whether to provide the noxious stimulus at a fixed intensity so that everyone receives the identical input. Each approach has unique pros and cons which need to be considered to i) accurately design an experiment, ii) enhance statistical inference in the given data and, iii) reduce bias and the influence of confounding factors in the individual study e.g., body composition, differences in energy absorption and previous experience. Individualization requires calibration, a procedure already irritating the nociceptive system but allowing to match the pain level across individuals. It leads to a higher variability of the stimulus intensity, thereby influencing the encoding of "noxiousness" by the central nervous system. Results might be less influenced by statistical phenomena such as ceiling/floor effects and the approach does not seem to rise ethical concerns. On the other hand, applying a fixed (standardized) intensity reduces the problem of intensity encoding leading to a large between-subjects variability in pain responses. Fixed stimulation intensities do not require pre-exposure. It can be proposed that one method is not preferable over another, however the choice depends on the study aim and the desired level of external validity. This paper discusses considerations for choosing the optimal approach for experimental pain studies and provides recommendations for different study designs. PERSPECTIVE: To calibrate pain or not? This dilemma is related to almost every experimental pain research. The decision is a trade-off between statistical power and greater control of stimulus encoding. The article decomposes both approaches and presents the pros and cons of either approach supported by data and simulation experiment.

Temporal properties of pain contrast enhancement using repetitive stimulation.

BACKGROUND: Offset analgesia (OA) is characterized by a disproportionately large reduction in pain following a small decrease in noxious stimulation and is based on temporal pain contrast enhancement (TPCE). The underlying mechanisms of this phenomenon are still poorly understood. This study is aiming to investigate whether TPCE can also be induced by repetitive stimulation, i.e., by stimuli clearly separated in time. METHODS: A repetitive TPCE paradigm was induced in healthy, pain-free subjects (n = 33) using heat stimuli. Three different interstimulus intervals (ISIs) were used: 5, 15, and 25 s. All paradigms were contrasted with a control paradigm without temperature change. Participants continuously rated perceived pain intensity. In addition, electrodermal activity (EDA) was recorded as a surrogate measure of autonomic arousal. RESULTS: Temporal pain contrast enhancement was confirmed for ISI 5 s (p < 0.001) and ISI 15 s (p = 0.005) but not for ISI 25 s (p = 0.07), however, the magnitude of TPCE did not differ between ISIs (p = 0.11). A TPCE-like effect was also detected with increased EDA values. CONCLUSIONS: TPCE can be induced by repetitive stimulation. This finding may be explained by a combination of the mechanisms underlying the OA and a facilitated pain habituation. SIGNIFICANCE: This experiment shows for the first time that temporal contrast enhancement of pain can be elicited by stimuli that are clearly separated in time with an interstimulus interval below 25 s.

Comparison of Feature Extraction Methods for Physiological Signals for Heat-Based Pain Recognition.

While even the most common definition of pain is under debate, pain assessment has remained the same for decades. But the paramount importance of precise pain management for successful healthcare has encouraged initiatives to improve the way pain is assessed. Recent approaches have proposed automatic pain evaluation systems using machine learning models trained with data coming from behavioural or physiological sensors. Although yielding promising results, machine learning studies for sensor-based pain recognition remain scattered and not necessarily easy to compare to each other. In particular, the important process of extracting features is usually optimised towards specific datasets. We thus introduce a comparison of feature extraction methods for pain recognition based on physiological sensors in this paper. In addition, the PainMonit Database (PMDB), a new dataset including both objective and subjective annotations for heat-induced pain in 52 subjects, is introduced. In total, five different approaches including techniques based on feature engineering and feature learning with deep learning are evaluated on the BioVid and PMDB datasets. Our studies highlight the following insights: (1) Simple feature engineering approaches can still compete with deep learning approaches in terms of performance. (2) More complex deep learning architectures do not yield better performance compared to simpler ones. (3) Subjective self-reports by subjects can be used instead of objective temperature-based annotations to build a robust pain recognition system.

AI Approaches Towards Prechtl's Assessment of General Movements: A Systematic Literature Review.

General movements (GMs) are spontaneous movements of infants up to five months post-term involving the whole body varying in sequence, speed, and amplitude. The assessment of GMs has shown its importance for identifying infants at risk for neuromotor deficits, especially for the detection of cerebral palsy. As the assessment is based on videos of the infant that are rated by trained professionals, the method is time-consuming and expensive. Therefore, approaches based on Artificial Intelligence have gained significantly increased attention in the last years. In this article, we systematically analyze and discuss the main design features of all existing technological approaches seeking to transfer the Prechtl's assessment of general movements from an individual visual perception to computer-based analysis. After identifying their shared shortcomings, we explain the methodological reasons for their limited practical performance and classification rates. As a conclusion of our literature study, we conceptually propose a methodological solution to the defined problem based on the groundbreaking innovation in the area of Deep Learning.