Prediction of pain responses to subsequent cold pressor test via baseline heart rate variability in healthy adults

Comparative Neurological and Behavioral Assessment of Central and Peripheral Stimulation Technologies for Induced Pain and Cognitive Tasks

Pain is a multifaceted, multisystem disorder that adversely affects neuro-psychological processes. This study compares the effectiveness of central stimulation (transcranial direct current stimulation-tDCS over F3/F4) and peripheral stimulation (transcutaneous electrical nerve stimulation-TENS over the median nerve) in pain inhibition during a cognitive task in healthy volunteers and to observe potential neuro-cognitive improvements. Eighty healthy participants underwent a comprehensive experimental protocol, including cognitive assessments, the Cold Pressor Test (CPT) for pain induction, and tDCS/TENS administration. EEG recordings were conducted pre- and post-intervention across all conditions. The protocol for this study was categorized into four groups: G1 (control), G2 (TENS), G3 (anodal-tDCS), and G4 (cathodal-tDCS). Paired t-tests (p < 0.05) were conducted to compare Pre-Stage, Post-Stage, and neuromodulation conditions, with t-values providing insights into effect magnitudes. The result showed a reduction in pain intensity with TENS (p = 0.002, t-value = -5.34) and cathodal-tDCS (p = 0.023, t-value = -5.08) and increased pain tolerance with TENS (p = 0.009, t-value = 4.98) and cathodal-tDCS (p = 0.001, t-value = 5.78). Anodal-tDCS (p = 0.041, t-value = 4.86) improved cognitive performance. The EEG analysis revealed distinct neural oscillatory patterns across the groups. Specifically, G2 and G4 showed delta-power reductions, while G3 observed an increase. Moreover, G2 exhibited increased theta-power in the occipital region during CPT and Post-Stages. In the alpha-band, G2, G3, and G4 had reductions Post-Stage, while G1 and G3 increased. Additionally, beta-power increased in the frontal region for G2 and G3, contrasting with a reduction in G4. Furthermore, gamma-power globally increased during CPT1, with G1, G2, and G3 showing reductions Post-Stage, while G4 displayed a global decrease. The findings confirm the efficacy of TENS and tDCS as possible non-drug therapeutic alternatives for cognition with alleviation from pain.

Prediction of pain using electrocardiographic-derived autonomic measures: A systematic review

BACKGROUND AND OBJECTIVE

Pain is a major clinical challenge, and understanding the pathophysiology is critical for optimal management. The autonomic nervous system reacts to pain stimuli, and autonomic dysfunction may predict pain sensation. The most used assessment of autonomic function is based on electrocardiographic measures, and the ability of such measures to predict pain was investigated.

DATABASES AND DATA TREATMENT

English articles indexed in PubMed and EMBASE were reviewed for eligibility and included when they reported electrocardiographic-derived measures' ability to predict pain response. The quality in prognostic studies (QUIPS) tool was used to assess the quality of the included articles.

RESULTS

The search revealed 15 publications, five on experimental pain, five on postoperative pain, and five on longitudinal clinical pain changes, investigating a total of 1069 patients. All studies used electrocardiographically derived parameters to predict pain assessed with pain thresholds using quantitative sensory testing or different scales. Across all study modalities, electrocardiographic measures were able to predict pain. Higher parasympathetic activity predicted decreased experimental, postoperative, and long-term pain in most cases while changes in sympathetic activity did not consistently predict pain.

CONCLUSIONS

Most studies demonstrated that parasympathetic activity could predict acute and chronic pain intensity. In the clinic, this may be used to identify which patients need more intensive care to prevent, for example postoperative pain and develop personalized chronic pain management.

SIGNIFICANCE

Pain is a debilitating problem, and the ability to predict occurrence and severity would be a useful clinical tool. Basal autonomic tone has been suggested to influence pain perception. This systematic review investigated electrocardiographic-derived autonomic tone and found that increased parasympathetic tone could predict pain reduction in different types of pain.

Measurements of Electrodermal Activity, Tissue Oxygen Saturation, and Visual Analog Scale for Different Cuff Pressures

The quantification of comfort in binding parts, essential human-machine interfaces (HMI) for the functioning of rehabilitation robots, is necessary to reduce physical strain on the user despite great achievements in their structure and control. This study aims to investigate the physiological impacts of binding parts by measuring electrodermal activity (EDA) and tissue oxygen saturation (StO2). In Experiment 1, EDA was measured from 13 healthy subjects under three different pressure conditions (10, 20, and 30 kPa) for 1 min using a pneumatic cuff on the right thigh. In Experiment 2, EDA and StO2 were measured from 10 healthy subjects for 5 min. To analyze the correlation between EDA parameters and the decrease in StO2, a survey using the visual analog scale (VAS) was conducted to assess the level of discomfort at each pressure. The EDA signal was decomposed into phasic and tonic components, and the EDA parameters were extracted from these two components. RM ANOVA and a post hoc paired t-test were used to determine significant differences in parameters as the pressure increased. The results showed that EDA parameters and the decrease in StO2 significantly increased with the pressure increase. Among the extracted parameters, the decrease in StO2 and the mean SCL proved to be effective indicators. Such analysis outcomes would be highly beneficial for studies focusing on the comfort assessment of the binding parts of rehabilitation robots.

Automatic detection of pain using machine learning

Pain is one of the most common symptoms reported by individuals presenting to hospitals and clinics and is associated with significant disability and economic impacts; however, the ability to quantify and monitor pain is modest and typically accomplished through subjective self-report. Since pain is associated with stereotypical physiological alterations, there is potential for non-invasive, objective pain measurements through biosensors coupled with machine learning algorithms. In the current study, a physiological dataset associated with acute pain induction in healthy adults was leveraged to develop an algorithm capable of detecting pain in real-time and in natural field environments. Forty-one human subjects were exposed to acute pain through the cold pressor test while being monitored using electrocardiography. A series of respiratory and heart rate variability features in the time, frequency, and nonlinear domains were calculated and used to develop logistic regression classifiers of pain for two scenarios: (1) laboratory/clinical use with an F1 score of 81.9% and (2) field/ambulatory use with an F1 score of 79.4%. The resulting pain algorithms could be leveraged to quantify acute pain using data from a range of sources, such as ECG data in clinical settings or pulse plethysmography data in a growing number of consumer wearables. Given the high prevalence of pain worldwide and the lack of objective methods to quantify it, this approach has the potential to identify and better mitigate individual pain.