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1. Objective Pain Measurement based on Physiological Signals

   https://doi.org/10.1177/2327857918071056  

   Lin, Yingzi; Wang, Li; Xiao, Yan; Urman, Richard D.; Dutton, Richard; Ramsay, Michael (June 2018, Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care)

   In an attempt to understand human physiological signals when an individual is subjected to pain, we set up a tonic pain experiment in a laboratory setting. The subjects’ physiological signals were recorded, timestamped, and compared to an initial 30 second baseline measurement. Subjects were also asked to verbally state their level of pain based on a visual analog scale in order to compare reported pain levels with physiological signals. The physiological signals measured were: Electroencephalography (EEG), Pupillary Unrest Under Ambient Light (PUAL), Skin Conductance (SC), Electromyography (EMG), Respiration Rate (RR), Blood Volume Pulse (BVP), Skin Temperature (ST), Blood Pressure (BP), and Facial Expression (FE). ANOVA and frequency domain analyses were conducted on the data in order to determine whether there was a significant difference between the ‘pain’ and ‘no pain’ (baseline) states of an individual. Based on our results, skin conductance, PUAL, facial expression, and EEG signals were theorized to be good signals for the classification of tonic pain, or any pain applied directly to an individual. 

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2. Experimental Exploration of Objective Human Pain Assessment Using Multimodal Sensing Signals

   https://doi.org/10.3389/fnins.2022.831627  

   Lin, Yingzi; Xiao, Yan; Wang, Li; Guo, Yikang; Zhu, Wenchao; Dalip, Biren; Kamarthi, Sagar; Schreiber, Kristin L.; Edwards, Robert R.; Urman, Richard D. (February 2022, Frontiers in Neuroscience)

   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. 

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3. A Novel Labeling Method of Physiological-based Pressure Pain Assessment Among Patients With and Without Chronic Low Back Pain

   https://doi.org/10.1177/10711813241284506  

   Zhu, Wenchao; Xiao, Yan; Lin, Yingzi (October 2024, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Pain, especially chronic pain, is a complicated and subjective experience, threatening global healthcare as one of the most severe health problems. Traditionally, pain is assessed by Visual Analog Scale to indicate the pain intensity by the patient’s self-report, causing them to become biased by various psychosocial factors. In this study, we performed two distinct labeling methods to assess the pressure pain in Quantitative Sensory Testing and to differentiate healthy controls and chronic low back pain patients: time period labels and percentage timestamp labels. Physiological signals such as blood volume pulse and galvanic skin response were collected. The time period labeling method was to segment via fixed time windows. The percentage timestamp labeling method was to select the timestamp labels based on the percentage of the threshold or the tolerance time. Both methods demonstrate different advantages when visualizing the information of different pain states and different participant groups. 

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4. A Computational View of the Emotional Regulation of Disgust using Multimodal Sensors

   https://doi.org/10.1109/FG47880.2020.00145  

   Kota, Vidhathri; Gali, Geeta Madhav; Nwogu, Ifeoma (November 2020, 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020))

   null (Ed.)

   Emotion regulation can be characterized by different activities that attempt to alter an emotional response, whether behavioral, physiological or neurological. The two most widely adopted strategies, cognitive reappraisal and expressive suppression are explored in this study, specifically in the context of disgust. Study participants (N = 21) experienced disgust via video exposure, and were instructed to either regulate their emotions or express them freely. If regulating, they were required to either cognitively reappraise or suppress their emotional experiences while viewing the videos. Video recordings of the participants' faces were taken during the experiment and electrocardiogram (ECG), electromyography (EMG), and galvanic skin response (GSR) readings were also collected for further analysis. We compared the participants behavioral (facial musculature movements) and physiological (GSR and heart rate) responses as they aimed to alter their emotional responses and computationally determined that when responding to disgust stimuli, the signals recorded during suppression and free expression were very similar, whereas those recorded during cognitive reappraisal were significantly different. Thus, in the context of this study, from a signal analysis perspective, we conclude that emotion regulation via cognitive reappraisal significantly alters participants' physiological responses to disgust, unlike regulation via suppression. 

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5. Exploration of physiological sensors, features, and machine learning models for pain intensity estimation

   https://doi.org/10.1371/journal.pone.0254108  

   Pouromran, Fatemeh; Radhakrishnan, Srinivasan; Kamarthi, Sagar (July 2021, PLOS ONE)

   Le, Khanh N.Q. (Ed.)

   In current clinical settings, typically pain is measured by a patient’s self-reported information. This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. This study uses BioVid Heat Pain Dataset. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain. We built different machine learning models, including Linear Regression, Support Vector Regression (SVR), Neural Networks and Extreme Gradient Boosting for continuous value pain intensity estimation. Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. We found that EDA is the most information-rich sensor for continuous pain intensity prediction. A set of only 3 features from EDA signals using SVR model gave an average performance of 0.93 mean absolute error (MAE) and 1.16 root means square error (RMSE) for the subject-independent model and of 0.92 MAE and 1.13 RMSE for subject-dependent. The MAE achieved with signal-feature-model combination is less than 1 unit on 0 to 4 continues pain scale, which is smaller than the MAE achieved by the methods reported in the literature. These results demonstrate that it is possible to estimate pain intensity of a patient using a computationally inexpensive machine learning model with 3 statistical features from EDA signal which can be collected from a wrist biosensor. This method paves a way to developing a wearable pain measurement device. 

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