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... this present work, nociceptive level of pain is perceived from the encapsulated electromyogram (EMG) signal from corrugator muscle (see Fig. 1)of the subjects exposed with heat stimuli using BioVid heat pain database [7] (explained in next section). Variation of EMG being a non-stationary phenomena, thus a data adaptive technique like Empirical Mode Decomposition (EMD) is used for feature extraction. Various levels of nociception were realized in two ways. Firstly, using ...

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... Diverse phases of heat pain were confirmed using bio-signals such as EMG, ECG, GSR, EEG, etc. [10][11][12]; additionally, the transformation of the facial appearance was noted using video signals during various pain levels [3]. Studies have been done to show the association between pain and the actions of facial muscles. ...
... The optimal pain classification using fEMG signals is shown by applying the selected fEMG features as the input, and the pain stage PA1-PA4 as the output for each EMG-zygomaticus and EMG-corrugator, as illustrated in Figure 4. Alternatively, the activity of electromyography (EMG) signals, in particular of the zygomatic and corrugator muscles, could provide information on pain intensity [12,35]. Part of the experimental sequence on which we evaluated the EMG signals were filtered with a Butterworth bandpass filter . ...
... Alternatively, the activity of electromyography (EMG) signals, in particular of the zygomatic and corrugator muscles, could provide information on pain intensity [12,35]. Part of the experimental sequence on which we evaluated the EMG signals were filtered with a Butterworth bandpass filter (20-250 Hz). ...
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The real-time recognition of pain level is required to perform an accurate pain assessment of patients in the intensive care unit, infants, and other subjects who may not be able to communicate verbally or even express the sensation of pain. Facial expression is a key pain-related behavior that may unlock the answer to an objective pain measurement tool. In this work, a machine learning-based pain level classification system using data collected from facial electromyograms (EMG) is presented. The dataset was acquired from part of the BioVid Heat Pain database to evaluate facial expression from an EMG corrugator and EMG zygomaticus and an EMG signal processing and data analysis flow is adapted for continuous pain estimation. The extracted pain-associated facial electromyography (fEMG) features classification is performed by K-nearest neighbor (KNN) by choosing the value of k which depends on the nonlinear models. The presentation of the accuracy estimation is performed, and considerable growth in classification accuracy is noticed when the subject matter from the features is omitted from the analysis. The ML algorithm for the classification of the amount of pain experienced by patients could deliver valuable evidence for health care providers and aid treatment assessment. The proposed classification algorithm has achieved a 99.4% accuracy for classifying the pain tolerance level from the baseline (P0 versus P4) without the influence of a subject bias. Moreover, the result on the classification accuracy clearly shows the relevance of the proposed approach.
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Although researchers have documented behavioral and brain structure correlates of pain resilience, associated physiological responses have received little consideration. In this study, we assessed psychophysiological differences between high (HPR), moderate (MPR), and low (LPR) pain resilience subgroups during anticipation, experiencing, and recovery from laboratory pain. In an initial pain anticipation task, participants (79 women, 32 man) viewed visual cues to signal possible mild or intense shocks prior to receiving these shocks. Subsequently, in a pain recovery task, participants received uncued mild and intense shocks. Subjective appraisals were assessed during each task in tandem with continuous recording of skin conductance level (SCL), heart rate variability (HRV), and corrugator electromyography (cEMG). On physiological indexes, HPR subgroup members displayed significantly lower SCL than MPR and LPR subgroups did during anticipation and experiencing of pain while no resilience group effects were found for HRV or cEMG. During pain recovery, HPR and LPR subgroups displayed weaker SCL than the MPR subgroup did in the immediate aftermath of shock. However, HPR members continued to display lower SCL than other groups did over an extended recovery period. On self-report measures, the LPR subgroup reported higher levels of anticipatory anxiety and expected pain than HPR and MPR subgroups did during the pain anticipation task. Together, results suggested higher pain resilience is characterized, in part, by comparatively reduced SCL during the course of anticipating, experiencing and recovering from painful shock.