Unconscious activation of negative emotional memories increases pain unpleasantness.

OBJECTIVE: The influence of unconscious emotional processes on pain remains poorly understood. The present study tested whether cues to forgotten unpleasant images might amplify pain (i.e., in the absence of conscious recall). METHODS: 72 healthy female adults (19 to 34 years) performed an adapted Think/No-think paradigm (T/NT) using 72 combinations of neutral face images (cues) paired with 36 neutral & 36 unpleasant images. After completion of the T/NT task, cues associated with forgotten neutral or unpleasant images were identified. Cues to either neutral or unpleasant images from the NT condition were then presented in randomized order while participants received intermediate-level thermal pain stimulation on the left hand. Ratings of both pain intensity and unpleasantness were acquired after each trial. RESULTS: Mean pain unpleasantness ratings were greater during presentation of cues to forgotten negative vs. neutral images (5.52 [SD = 2.06] vs. 5.23 [SD = 2.10]; p = 0.02). This pattern was also present when comparing cues to remembered negative vs. neutral images (5.62 [SD = 1.94] vs. 5.04 [SD = 1.90]; p < .001). Mean pain intensity ratings were higher for cues to negative vs. neutral images when remembered (5.48 [SD = 1.79] vs. 5.00 [SD = 1.69]; p < .001), but not when forgotten (5.27 [SD = 1.96] vs. 5.16 [SD = 1.93]; p = 0.30). CONCLUSIONS: Using an adapted T/NT-Pain paradigm, this study demonstrated that cues to non-recallable (but potentially unconsciously activated) negative emotional memories amplify pain unpleasantness, similar to known effects of conscious negative emotions.

Automated Electrodermal Activity and Facial Expression Analysis for Continuous Pain Intensity Monitoring on the X-ITE Pain Database.

This study focuses on improving healthcare quality by introducing an automated system that continuously monitors patient pain intensity. The system analyzes the Electrodermal Activity (EDA) sensor modality modality, compares the results obtained from both EDA and facial expressions modalities, and late fuses EDA and facial expressions modalities. This work extends our previous studies of pain intensity monitoring via an expanded analysis of the two informative methods. The EDA sensor modality and facial expression analysis play a prominent role in pain recognition; the extracted features reflect the patient's responses to different pain levels. Three different approaches were applied: Random Forest (RF) baseline methods, Long-Short Term Memory Network (LSTM), and LSTM with the sample-weighting method (LSTM-SW). Evaluation metrics included Micro average F1-score for classification and Mean Squared Error (MSE) and intraclass correlation coefficient (ICC [3, 1]) for both classification and regression. The results highlight the effectiveness of late fusion for EDA and facial expressions, particularly in almost balanced datasets (Micro average F1-score around 61%, ICC about 0.35). EDA regression models, particularly LSTM and LSTM-SW, showed superiority in imbalanced datasets and outperformed guessing (where the majority of votes indicate no pain) and baseline methods (RF indicates Random Forest classifier (RFc) and Random Forest regression (RFr)). In conclusion, by integrating both modalities or utilizing EDA, they can provide medical centers with reliable and valuable insights into patients' pain experiences and responses.

The impact of analgesic on EMG and other biosignals in a postoperative setting.

Background: In the clinical context, the assessment of pain in patients with inadequate communication skills is standardly performed externally by trained medical staff. Automated pain recognition (APR) could make a significant contribution here. Hereby, pain responses are captured using mainly video cams and biosignal sensors. Primary, the automated monitoring of pain during the onset of analgesic sedation has the highest relevance in intensive care medicine. In this context, facial electromyography (EMG) represents an alternative to recording facial expressions via video in terms of data security. In the present study, specific physiological signals were analyzed to determine, whether a distinction can be made between pre-and post-analgesic administration in a postoperative setting. Explicitly, the significance of the facial EMG regarding the operationalization of the effect of analgesia was tested. Methods: N = 38 patients scheduled for surgical intervention where prospectively recruited. After the procedure the patients were transferred to intermediate care. Biosignals were recorded and all doses of analgesic sedations were carefully documented until they were transferred back to the general ward. Results: Almost every biosignal feature is able to distinguish significantly between 'before' and 'after' pain medication. We found the highest effect sizes (r = 0.56) for the facial EMG. Conclusion: The results of the present study, findings from research based on the BioVid and X-ITE pain datasets, staff and patient acceptance indicate that it would now be appropriate to develop an APR prototype.

Measurement of the nociceptive flexion reflex threshold in critically ill patients - a randomized observational pilot study.

BACKGROUND: Pain detection and treatment is a major challenge in the care of critically ill patients, rendered more complex by the need to take into consideration the risk of insufficient or excessive analgesia. The nociceptive flexion reflex threshold (NFRT) has become the established basis for measuring the level of analgesia in the perioperative context. However, it remains unclear whether NFRT measurement can be usefully applied to mechanically ventilated, analgosedated critically ill patients who are unable to communicate. Therefore, the aim of the present study was to investigate whether there is an association between the NFRT measurement and the Behavioral Pain Scale (BPS) in critically ill, analgosedated, and mechanically ventilated patients and whether the NFRT measurement can also detect potential excessive analgesia. METHODS: This prospective, observational, randomized single-center pilot study included patients admitted to the surgical Intensive Care Unit of University Hospital Ulm, Germany, all of whom were analgosedated and intubated. Major exclusion criteria were defined as the need for the administration of neuromuscular blocking agents or neurological diseases associated with peripheral nerve conduction restriction. Initial NFRT and BPS measurements were conducted within 12 h after admission. A structured pain assessment was performed at least twice daily until extubation throughout the observation period thereafter (Group A: BPS + NFRT, Group B: BPS). RESULTS: 114 patients were included in the study. NFRT is associated negatively with BPS. NFRT was almost twice as high in patients with a Richmond Agitation Sedation Scale (RASS) score of -5 than in patients with a RASS score ≥ -4 (RASS -5 - NFRT: 59.40 vs. RASS -4 - NFRT: 29.00, p < 0.001). CONCLUSIONS: NFRT measurement is associated negatively with the BPS in critically ill patients. NFRT measurement provides guidance for the evaluation of nociceptive processes in patients with RASS scores ≤ -4, in whom analgesia level is often difficult to assess. However, in order to identify excessive analgesia and derive therapeutic consequences, it is necessary to gradually decrease analgesics and sedatives until a stimulus threshold is reached at which the patient does not feel pain. TRIAL REGISTRATION: Retrospectively registered in the German Clinical Trials Register, registration number DRKS00021149, date of registration: March 26, 2020. https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00021149 .

Autonomous Nervous Response During Sedation in Colonoscopy and the Relationship With Clinician Satisfaction.

Background: Nurse assisted propofol sedation (NAPS) is a common method used for colonoscopies. It is safe and widely accepted by patients. Little is known, however, about the satisfaction of clinicians performing colonoscopies with NAPS and the factors that negatively influence this perception such as observer-reported pain events. In this study, we aimed to correlate observer-reported pain events with the clinicians' satisfaction with the procedure. Additionally, we aimed to identify patient biosignals from the autonomic nervous system (B-ANS) during an endoscopy that correlate with those pain events. Methods: Consecutive patients scheduled for a colonoscopy with NAPS were prospectively recruited. During the procedure, observer-reported pain events, which included movements and paralinguistic sounds, were simultaneously recorded with different B-ANS (facial electromyogram (EMG), skin conductance level, body temperature and electrocardiogram). After the procedure, the examiners filled out the Clinician Satisfaction with Sedation Instrument (CSSI). The primary endpoint was the correlation between CSSI and observer-reported pain events. The second primary endpoint was the identification of B-ANS that make it possible to predict those events. Secondary endpoints included the correlation between CSSI and sedation depth, the frequency and dose of sedative use, polyps resected, resection time, the duration of the procedure, the time it took to reach the coecum and the experience of the nurse performing the NAPS. ClinicalTrials.gov: NCT03860779. Results: 112 patients with 98 (88.5%) available B-ANS recordings were prospectively recruited. There was a significant correlation between an increased number of observer-reported pain events during an endoscopy with NAPS and a lower CSSI (r = -0.318, p = 0.001). Additionally, the EMG-signal from facial muscles correlated best with the event time points, and the signal significantly exceeded the baseline 30 s prior to the occurrence of paralinguistic sounds. The secondary endpoints showed that the propofol dose relative to the procedure time, the cecal intubation time, the time spent on polyp removal and the individual nurse performing the NAPS significantly correlated with CSSI. Conclusion: This study shows that movements and paralinguistic sounds during an endoscopy negatively correlate with the satisfaction of the examiner measured with the CSSI. Additionally, an EMG of the facial muscles makes it possible to identify such events and potentially predict their occurrence.

Automatic vs. Human Recognition of Pain Intensity from Facial Expression on the X-ITE Pain Database.

Prior work on automated methods demonstrated that it is possible to recognize pain intensity from frontal faces in videos, while there is an assumption that humans are very adept at this task compared to machines. In this paper, we investigate whether such an assumption is correct by comparing the results achieved by two human observers with the results achieved by a Random Forest classifier (RFc) baseline model (called RFc-BL) and by three proposed automated models. The first proposed model is a Random Forest classifying descriptors of Action Unit (AU) time series; the second is a modified MobileNetV2 CNN classifying face images that combine three points in time; and the third is a custom deep network combining two CNN branches using the same input as for MobileNetV2 plus knowledge of the RFc. We conduct experiments with X-ITE phasic pain database, which comprises videotaped responses to heat and electrical pain stimuli, each of three intensities. Distinguishing these six stimulation types plus no stimulation was the main 7-class classification task for the human observers and automated approaches. Further, we conducted reduced 5-class and 3-class classification experiments, applied Multi-task learning, and a newly suggested sample weighting method. Experimental results show that the pain assessments of the human observers are significantly better than guessing and perform better than the automatic baseline approach (RFc-BL) by about 1%; however, the human performance is quite poor due to the challenge that pain that is ethically allowed to be induced in experimental studies often does not show up in facial reaction. We discovered that downweighting those samples during training improves the performance for all samples. The proposed RFc and two-CNNs models (using the proposed sample weighting) significantly outperformed the human observer by about 6% and 7%, respectively.

Evaluation of an Objective Measurement Tool for Stress Level Reduction by Individually Chosen Music During Colonoscopy-Results From the Study "ColoRelaxTone".

Background and Aims: Colonoscopy as standard procedure in endoscopy is often perceived as uncomfortable for patients. Patient's anxiety is therefore a significant issue, which often lead to avoidance of participation of relevant examinations as CRC-screening. Non-pharmacological anxiety management interventions such as music might contribute to relaxation in the phase prior and during endoscopy. Although music's anxiolytic effects have been reported previously, no objective measurement of stress level reduction has been reported yet. Focus of this study was to evaluate the objective measurement of the state of relaxation in patients undergoing colonoscopy. Methods: Prospective study (n = 196) performed at one endoscopic high-volume center. Standard colonoscopy was performed in control group. Interventional group received additionally self-chosen music over earphones. Facial Electromyography (fEMG) activity was obtained. Clinician Satisfaction with Sedation Instrument (CSSI) and Patients Satisfaction with Sedation Instrument (PSSI) was answered by colonoscopists and patients, respectively. Overall satisfaction with music accompanied colonoscopy was obtained if applicable. Results: Mean difference measured by fEMG via musculus zygomaticus major indicated a significantly lower stress level in the music group [7.700(±5.560) µV vs. 4.820(±3.330) µV; p = 0.001]. Clinician satisfaction was significantly higher with patients listening to music [82.69(±15.04) vs. 87.3(±15.02) pts.; p = 0.001]. Patient's satisfaction was higher but did not differ significantly. Conclusions: We conclude that self-chosen music contributes objectively to a reduced stress level for patients and therefore subjectively perceived satisfaction for endoscopists. Therefore, music should be considered as a non-pharmacological treatment method of distress reduction especially in the beginning of endoscopic procedures.

Effects of Expectancy on Cognitive Performance, Mood, and Psychophysiology in Healthy Adolescents and Their Parents in an Experimental Study.

OBJECTIVE: Placebo effects on cognitive performance and mood and their underlying mechanisms have rarely been investigated in adolescents. Therefore, the following hypotheses were investigated with an experimental paradigm: (1) placebo effects could be larger in adolescents than in adults, (2) parents' expectations influence their adolescents' expectations and placebo effects, and (3) a decrease in stress levels could be an underlying mechanism of placebo effects. METHODS: Twenty-six healthy adolescents (13.8 ± 1.6 years, 14 girls) each with a parent (45.5 ± 4.2 years, 17 mothers) took part in an experimental within-subjects study. On two occasions, a transdermal patch was applied to their hips and they received an envelope containing either the information that it is a Ginkgo patch to improve cognitive performance and mood, or it is an inactive placebo patch, in counterbalanced order. Cognitive performance and mood were assessed with a parametric Go/No-Go task (PGNG), a modification of California Verbal Learning Test, and Profile of Mood Scales (POMS). Subjects rated their expectations about Ginkgo's effects before patch application as well as their subjective assessment of its effects after the tests. An electrocardiogram and skin conductance levels (SCLs) were recorded and root mean square of successive differences (RMSSD), high-frequency power (HF), and the area under the curve of the SCL (AUC) were analyzed as psychophysiological stress markers. RESULTS: Expectations did not differ between adolescents and parents and were correlated concerning reaction times only. Overall, expectations did not influence placebo effects. There was only one significant placebo effect on the percentage of correct inhibited trials in one level of the PGNG in adolescents, but not in parents. RMSSD and HF significantly increased, and AUC decreased from pre- to post-patch application in adolescents, but not in parents. CONCLUSION: With this experimental paradigm, we could not induce relevant placebo effects in adolescents and parents. This could be due to aspects of the study design such as application form and substance, and that healthy subjects were employed. Nevertheless, we could show that adolescents are more sensitive to psychophysiological reactions related with interventions which could be part of the underlying mechanisms of placebo effects in adolescents.

"What About Automated Pain Recognition for Routine Clinical Use?" A Survey of Physicians and Nursing Staff on Expectations, Requirements, and Acceptance.

Background: Over the last 12 years, the fundamentals of automated pain recognition using artificial intelligence (AI) algorithms have been investigated and optimized. The main target groups are patients with limited communicative abilities. To date, the extent to which anesthetists and nurses in intensive care units would benefit from an automated pain recognition system has not been investigated. Methods: N = 102 clinical employees were interviewed. To this end, they were shown a video in which the visionary technology of automated pain recognition, its basis and goals are outlined. Subsequently, questions were asked about: (1) the potential benefit of an automated pain recognition in clinical context, (2) preferences with regard to the modality used (physiological, paralinguistic, video-based, multimodal), (3) the maximum willingness to invest, (4) preferences concerning the required pain recognition rate and finally (5) willingness to use automated pain recognition. Results: The respondents expect the greatest benefit from an automated pain recognition system to be "to avoid over- or undersupply of analgesics in patients with limited communicative abilities," a total of 50% of respondents indicated that they would use automated pain recognition technology, 32.4% replied with "perhaps" and 17.4% would not use it. Conclusion: Automated pain recognition is, in principle, accepted by anesthetists and nursing staff as a possible new method, with expected benefits for patients with limited communicative skills. However, studies on automated pain recognition in a clinical environment and proof of its acceptance and practicability are absolutely necessary before such systems can be implemented.

Multi-Modal Signals for Analyzing Pain Responses to Thermal and Electrical Stimuli.

The assessment of pain relies mostly on methods that require a person to communicate. However, for people with cognitive and verbal impairments, existing methods are not sufficient as they lack reliability and validity. To approach this problem, recent research focuses on an objective pain assessment facilitated by parameters of responses derived from physiology, and video and audio signals. To develop reliable automated pain recognition systems, efforts have been made in creating multimodal databases in order to analyze pain and detect valid pain patterns. While the results are promising, they only focus on discriminating pain or pain intensities versus no pain. In order to advance this, research should also consider the quality and duration of pain as they provide additional valuable information for more advanced pain management. To complement existing databases and the analysis of pain regarding quality and length, this paper proposes a psychophysiological experiment to elicit, measure, and collect valid pain reactions. Participants are subjected to painful stimuli that differ in intensity (low, medium, and high), duration (5 s / 1 min), and modality (heat / electric pain) while audio, video (e.g., facial expressions, body gestures, facial skin temperature), and physiological signals (e.g., electrocardiogram [ECG], skin conductance level [SCL], facial electromyography [EMG], and EMG of M. trapezius) are being recorded. The study consists of a calibration phase to determine a subject's individual pain range (from low to intolerable pain) and a stimulation phase in which pain stimuli, depending on the calibrated range, are applied. The obtained data may allow refining, improving, and evaluating automated recognition systems in terms of an objective pain assessment. For further development of such systems and to investigate pain reactions in more detail, additional pain modalities such as pressure, chemical, or cold pain should be included in future studies. Recorded data of this study will be released as the "X-ITE Pain Database".

Affective Computing and the Impact of Gender and Age.

Affective computing aims at the detection of users' mental states, in particular, emotions and dispositions during human-computer interactions. Detection can be achieved by measuring multimodal signals, namely, speech, facial expressions and/or psychobiology. Over the past years, one major approach was to identify the best features for each signal using different classification methods. Although this is of high priority, other subject-specific variables should not be neglected. In our study, we analyzed the effect of gender, age, personality and gender roles on the extracted psychobiological features (derived from skin conductance level, facial electromyography and heart rate variability) as well as the influence on the classification results. In an experimental human-computer interaction, five different affective states with picture material from the International Affective Picture System and ULM pictures were induced. A total of 127 subjects participated in the study. Among all potentially influencing variables (gender has been reported to be influential), age was the only variable that correlated significantly with psychobiological responses. In summary, the conducted classification processes resulted in 20% classification accuracy differences according to age and gender, especially when comparing the neutral condition with four other affective states. We suggest taking age and gender specifically into account for future studies in affective computing, as these may lead to an improvement of emotion recognition accuracy.

Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines.

BACKGROUND: The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient's report on the pain sensation. Verbal scales, visual analog scales (VAS) or numeric rating scales (NRS) count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity. METHODS: In this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold) under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity. RESULTS: We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography. CONCLUSION: The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.

Automatic pain quantification using autonomic parameters

The objective measurement of subjective, multi-dimensionally experienced pain is a problem for which there has not been an adequate solution. Although verbal methods (e.g., pain scales and questionnaires) are commonly used to measure clinical pain, they tend to lack objectivity, reliability, or validity when applied to mentally impaired individuals. Biopotential and behavioral parameters may represent a solution. Such coding systems already exist, but they are either very costly or time-consuming or have not been sufficiently evaluated. In this context, we collected a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. For this purpose, participants were subjected to painful heat stimuli under controlled conditions. One hundred thirty-five features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, and variability. The following features were chosen as the most selective: (1) electromyography corrugator peak to peak, (2) corrugator shannon entropy, and (3) heart rate variability slope RR. Individual-specific calibration allows the adjustment of feature patterns, resulting in significantly more accurate pain detection rates. The objective measurement of pain in patients will provide valuable information for the clinical team, which may aid the objective assessment of treatment (e.g., effectiveness of drugs for pain reduction, information on surgical indication, and quality of care provided to patients).(AU)

Automatic pain quantification using autonomic parameters

The objective measurement of subjective, multi-dimensionally experienced pain is a problem for which there has not been an adequate solution. Although verbal methods (e.g., pain scales and questionnaires) are commonly used to measure clinical pain, they tend to lack objectivity, reliability, or validity when applied to mentally impaired individuals. Biopotential and behavioral parameters may represent a solution. Such coding systems already exist, but they are either very costly or time-consuming or have not been sufficiently evaluated. In this context, we collected a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. For this purpose, participants were subjected to painful heat stimuli under controlled conditions. One hundred thirty-five features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, and variability. The following features were chosen as the most selective: (1) electromyography corrugator peak to peak, (2) corrugator shannon entropy, and (3) heart rate variability slope RR. Individual-specific calibration allows the adjustment of feature patterns, resulting in significantly more accurate pain detection rates. The objective measurement of pain in patients will provide valuable information for the clinical team, which may aid the objective assessment of treatment (e.g., effectiveness of drugs for pain reduction, information on surgical indication, and quality of care provided to patients).

Similarities and differences of emotions in human-machine and human-human interactions: what kind of emotions are relevant for future companion systems?

Cognitive-technical intelligence is envisioned to be constantly available and capable of adapting to the user's emotions. However, the question is: what specific emotions should be reliably recognised by intelligent systems? Hence, in this study, we have attempted to identify similarities and differences of emotions between human-human (HHI) and human-machine interactions (HMI). We focused on what emotions in the experienced scenarios of HMI are retroactively reflected as compared with HHI. The sample consisted of N = 145 participants, who were divided into two groups. Positive and negative scenario descriptions of HMI and HHI were given by the first and second groups, respectively. Subsequently, the participants evaluated their respective scenarios with the help of 94 adjectives relating to emotions. The correlations between the occurrences of emotions in the HMI versus HHI were very high. The results do not support the statement that only a few emotions in HMI are relevant.

The influence of neuroticism and psychological symptoms on the assessment of images in three-dimensional emotion space.

OBJECTIVE: The present study investigated the influence of neuroticism (NEO Five-Factor Inventory (NEO-FFI)) and psychological symptoms (Brief Symptom Inventory (BSI)) on pleasure, arousal, and dominance (PAD) ratings of the International Affective Picture System (IAPS). METHODS: The subjects (N=131) were presented with images from the IAPS (30 images) and new images (30 images). The influence of neuroticism and BSI (median split: high vs. low) on the assessment of pleasure, arousal and dominance of the images was examined. Correlations of pleasure, arousal and dominance were presented in a 3-D video animation. RESULTS: Subjects with high scores (compared to subjects with low scores by median split) of neuroticism and psychological symptoms of the BSI rated the presented emotional images more negative in the valence dimension (pleasure), higher in arousal and less dominant. CONCLUSION: Neuroticism and psychological symptoms influence the subjective emotional evaluation of emotional images. Therefore the location in the three-dimensional emotion space depends on individual differences. Such differences must be kept in mind, if correlations between emotion ratings and other variables like psychobiological measures are analyzed.