Relevant bibliographies by topics / Pain detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Pain detection'

Author: Grafiati
Published: 4 May 2024

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Journal articles on the topic "Pain detection"

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FUJIMASA, I. "Pain Detection and its Treatment." JAPANES JOURNAL OF MEDICAL INSTRUMENTATION 55, no. 9 (September 1, 1985): 476–79. http://dx.doi.org/10.4286/ikakikaigaku.55.9_476.

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Alazrai, Rami, Saifaldeen AL-Rawi, Hisham Alwanni, and Mohammad I. Daoud. "Tonic Cold Pain Detection Using Choi–Williams Time-Frequency Distribution Analysis of EEG Signals: A Feasibility Study." Applied Sciences 9, no. 16 (August 20, 2019): 3433. http://dx.doi.org/10.3390/app9163433.

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Detecting pain based on analyzing electroencephalography (EEG) signals can enhance the ability of caregivers to characterize and manage clinical pain. However, the subjective nature of pain and the nonstationarity of EEG signals increase the difficulty of pain detection using EEG signals analysis. In this work, we present an EEG-based pain detection approach that analyzes the EEG signals using a quadratic time-frequency distribution, namely the Choi–Williams distribution (CWD). The use of the CWD enables construction of a time-frequency representation (TFR) of the EEG signals to characterize the time-varying spectral components of the EEG signals. The TFR of the EEG signals is analyzed to extract 12 time-frequency features for pain detection. These features are used to train a support vector machine classifier to distinguish between EEG signals that are associated with the no-pain and pain classes. To evaluate the performance of our proposed approach, we have recorded EEG signals for 24 healthy subjects under tonic cold pain stimulus. Moreover, we have developed two performance evaluation procedures—channel- and feature-based evaluation procedures—to study the effect of the utilized EEG channels and time-frequency features on the accuracy of pain detection. The experimental results show that our proposed approach achieved an average classification accuracy of 89.24% in distinguishing between the no-pain and pain classes. In addition, the classification performance achieved using our proposed approach outperforms the classification results reported in several existing EEG-based pain detection approaches.
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Siqueira, Silvia R. D. T. de, Mariana Siviero, Fábio K. Alvarez, Manoel J. Teixeira, and José T. T. de Siqueira. "Quantitative sensory testing in trigeminal traumatic neuropathic pain and persistent idiopathic facial pain." Arquivos de Neuro-Psiquiatria 71, no. 3 (March 2013): 174–79. http://dx.doi.org/10.1590/s0004-282x2013000300009.

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The objective of this article was to investigate, with a systematic protocol of quantitative sensory testing, patients with persistent idiopathic facial pain (PIFP) and others with trigeminal traumatic neuropathic pain (TTN) compared to controls. Thirty patients with PIFP, 19 with TTN, and 30 controls were evaluated on subjective numbness and dysesthesia and with a systematic protocol of quantitative sensory testing for thermal evaluation (cold and warm), mechanical detection (touch and pinpricks for mechanical pain), superficial pain thresholds, and corneal reflex. We found that PIFP and TTN had numbness and dysesthesia higher than controls (p<0.001 and p=0.003), and that in both of them mechanical pain by pinpricks detection was abnormal intra and extra orally at the mandibular branch (p<0.001). Cold, warm, and tactile detections and pain thresholds were similar among the groups. Corneal reflex was abnormal in TTN (p=0.005). This study supports neuropathic mechanisms involving pain processing in PIFP and that the criterion on absence of sensorial variations in PIFP should be revised.
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Ostermeyer, Georg Peter, and Frank Schiefer. "On Pain Detection in Multibody Systems." Applied Mechanics and Materials 9 (October 2007): 115–26. http://dx.doi.org/10.4028/www.scientific.net/amm.9.115.

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Up to now a coherence between pain and technical systems has almost not been researched. Whereas some aspects of the nociceptive pain which serves human beings as a warning system and is also described as useful, can be transferred necessarily to technical systems. The idea of pain detection is an additional product of the Mesoscopic Particle Method [2-4]. Thereby the transformation of kinetic energy into heat energy caused by impact- and friction processes in the boundary layer of contact areas is described correctly with respect to thermodynamics. Between the properties of pain and heat there obviously exist analogies. Pain increases when certain external effects get higher and decreases smoothly, when the effect is taken off [5]. Generally pain is a vector of different phenomena. By means of the developed sensor concept the application of energy is detected including implicit frequency selective information about the jerk. It will experimentally and numerically be shown how e. g. a “hard-soft-detection” of surfaces can be evaluated by the developed sensor concept.
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Mills, Daniel, and Lauren Finka. "Early detection of pain in cats." Veterinary Record 178, no. 18 (April 28, 2016): 456. http://dx.doi.org/10.1136/vr.i2429.

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Aasvang, Eske K., Bo Møhl, and Henrik Kehlet. "Ejaculatory Pain." Anesthesiology 107, no. 2 (August 1, 2007): 298–304. http://dx.doi.org/10.1097/01.anes.0000270736.28324.61.

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Background Sexual dysfunction due to ejaculatory and genital pain after groin hernia surgery may occur in approximately 2.5% of patients. However, the specific psychosexological and neurophysiologic characteristics have not been described, thereby precluding assessment of pathogenic mechanisms and treatment strategies. Methods Ten patients with severe pain-related sexual dysfunction and ejaculatory pain were assessed in detail by quantitative sensory testing and interviewed by a psychologist specialized in evaluating sexual functional disorders and were compared with a control group of 20 patients with chronic pain after groin hernia repair but without sexual dysfunction, to identify sensory changes associated with ejaculatory pain. Results Quantitative sensory testing showed significantly higher thermal and mechanical detection thresholds and lowered mechanical pain detection thresholds in both groups compared with the nonpainful side. Pressure pain detection threshold and tolerance were significantly lower in the ejaculatory pain group compared with the control group. 'The maximum pain was specifically located at the external inguinal annulus in all ejaculatory pain patients, but not in controls. The psychosexual interview revealed no major psychosexual disturbances and concluded that the pain was of somatic origin. All patients with ejaculatory pain had experienced major negative life changes and deterioration in their overall quality of life and sexual function as a result of the hernia operation. Conclusions Postherniotomy ejaculatory pain and pain-related sexual dysfunction is a specific chronic pain state that may be caused by pathology involving the vas deferens and/or nerve damage. Therapeutic strategies should therefore include neuropathic pain treatment and/or surgical exploration.
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Sriraam, N., and S. Tejaswini. "Infant Cry Detection and Pain Scale Assessment." International Journal of Biomedical and Clinical Engineering 3, no. 1 (January 2014): 42–51. http://dx.doi.org/10.4018/ijbce.2014010104.

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A biological alarm system that connects mother and new born is referred as infant cry. Infant cry is a first means of communication through which mother understands the level of distress/ needs. Infant cry can be considered a multimodal behavior which involves limb movements, facial expressions which changes over time to identify the needs of an infant. The cry of the baby cannot be predicted accurately and it is hard to identify for what it cries for. The infant's cry is mainly a vocal signal which is a way of communication that aims to get attention of the listener to a physical state like hunger, pain, discomfort, fear, illness, wet diaper etc., .Pain is one of the most common symptoms experienced world over. Pain is an unpleasant feeling that is conveyed to the brain by sensory neurons. The discomfort signals actual or potential injury to the body. This pilot study gives an insight on the current state of works in infant cry analysis and pain scale assessment and also concludes with thoughts about the future directions for better representation and interpretation of infant cry signals.
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Wooley, Charles F., Elizabeth H. Sparkas, and Harisios Boudoulas. "Aortic pain: The renaissance of cardiovascular pain and the detection of aortopathy." Herz 24, no. 2 (April 1999): 140–53. http://dx.doi.org/10.1007/bf03043853.

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Grothusen, John R. "Thermal Pain in Complex Regional Pain Syndrome Type I." Pain Physician 17;1, no. 1;17 (January 14, 2014): 71–79. http://dx.doi.org/10.36076/ppj.2014/17/71.

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Background: Quantitative sensory testing (QST), with thermal threshold determinations, is a routine part of the comprehensive clinical workup of patients suffering from chronic pain, especially those with Complex Regional Pain Syndrome seen at our outpatient pain clinic. This is done to quantitatively assess each patient’s small fiber and sensory function in a controlled manner. Most patients have normal sensory detection thresholds, but there are large differences in thermal pain thresholds. Some patients display no thermal hyperalgesia, while other patients display severe thermal hyperalgesia when tested in all 4 limbs. Objectives: To ascertain the prevalence of thermal hyperalgesia in patients with complex regional pain syndrome type 1 (CRPS-I). Study Design: This was a retrospective review of the results of QST performed on 105 patients as part of their clinical workup. Setting: The outpatient clinic of the Department of Neurology at Drexel University College of Medicine. Methods: All patients had a diagnosis of CRPS-I. Thermal quantitative sensory testing, including cool detection, warm detection, cold pain, and heat pain, was performed on 8 distal sites on each patient as part of a comprehensive clinical examination. Results: With regards to thermal hyperalgesia, patients with CPRS-I appear to fall into distinct groups. One subgroup displays evidence of generalized cold and heat hyperalgesia, one subgroup displays evidence of generalized cold hyperalgesia only, one displays evidence of heat hyperalgesia only, and one subgroup does not display evidence of cold or heat hyperalgesia. Limitations: This study is based on retrospective information on a relatively small (105 patient records) number of patients. Since only patients with CRPS-I were included, the results are only applicable to this group. Conclusions: Thermal QST provides useful information about the sensory phenotype of individual patients. Subgrouping based on thermal hyperalgesia may be useful for future studies regarding prognosis, treatment selection, and efficacy. Key words: Complex regional pain syndrome, CRPS, quantitative sensory testing, QST, cold pain, heat pain, thermal hyperalgesia
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Iqbal, Syma, James Walcott, and Stephen Chan. "Acute Myelomonocytic Leukemia Presenting as Perianal Pain." Case Reports in Surgery 2018 (November 27, 2018): 1–4. http://dx.doi.org/10.1155/2018/2151492.

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Extramedullary involvement of the gastrointestinal tract (GIT) is a rare entity as most patients present with lymphoreticular organ involvement. Its detection and diagnosis can be extremely challenging, as these patients would present with unusual clinical symptoms. We diagnosed and managed a patient with leukemic infiltration of GIT who presented with perianal pain. Prompt use of MRI played an important role in detecting underlying pathology, and effective tissue sampling confirmed the diagnosis. This resulted in overall successful management of the patient.
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Dissertations / Theses on the topic "Pain detection"

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Mahon, Mary L. "Pain perception in chronic pain patients : a signal detection analysis." Thesis, University of British Columbia, 1991. http://hdl.handle.net/2429/31127.

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The purpose of this investigation was to examine the supposition that chronic pain patients (CPPs) have altered pain perception. Two models were examined that led to opposing predictions as to how CPPs would respond to painful stimuli (i.e., the hypervigilance and adaptation-level models). Both predictions have been supported by past research but because of methodological variation and the type of pain disorder studied, it has remained unclear under what circumstances the predictions of these two models may be met. The responses of pain patients to painful stimuli have been found to vary for patients-with different clinical presentations (i.e. those with and without medically incongruent signs and symptoms). Therefore, the present investigation sought to compare the responses to radiant heat stimuli of sixty CPPs (thirty with and thirty without a medically incongruent pain presentation) to thirty age and sex matched normal control subjects (i.e., pain-tree individuals). Signal detection theory methodology was used in order to separately evaluate sensory sensitivity and the response bias to report sensations as painful. In addition, cognitive and affective factors were assessed in order to identity potential psychological correlates of altered pain perception. The results of this study indicated that the presence of a medically incongruent pain presentation distinguished patients on their subjective report of disability and to a lesser extent cognitive appraisal and affective distress regarding their pain condition. They did not differ in their responses to painful stimuli. In a post hoc analysis where CPPs were classified into 'organic' and 'functional’ diagnostic groups, significant differences in pain threshold and the response bias to report pain were found. Patients classified as 'organic' had significantly higher pain thresholds compared to normal control subjects and patients classified as 'functional'. Differences in pain threshold were primarily represented by the response' bias to report sensations as painful rather than sensory sensitivity to the stimuli. The 'functional' group had a slightly lower pain threshold than the normal control group but this difference was not significant. The results are discussed in light of the two models of pain perception. The two methods used to classify pain patients are discussed according to their orthogonal characteristics on sensory, cognitive, and affective components.
Arts, Faculty of
Psychology, Department of
Graduate
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Lloyd, Emily Paige. "Race Deficits in Pain Authenticity Detection." Miami University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=miami1531912112953475.

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Mettam, Jessica Jane. "The Detection and Alleviation of Pain in Fish." Thesis, University of Liverpool, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.526925.

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Miller, Amy Louise. "Detection and alleviation of pain and distress in laboratory rodents." Thesis, University of Newcastle Upon Tyne, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519633.

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Tan, Chee-Wee. "Signal detection theory in the study of nociceptive and pain perception processes." Thesis, Queen Margaret University, 2008. https://eresearch.qmu.ac.uk/handle/20.500.12289/7409.

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Signal detection theory (SDT) measures (discriminability and response bias) have been proposed to be valid for determining pain perception changes. The construct validity of SDT measures applied to pain perception studies has been questioned on three grounds: interpretation, methodology and theory. Multiple interpretations are possible for the combinations of discriminability and response bias change when the magnitude-rating scale is used for pain perception studies. This is resolved by utilising the confidence-rating scale. The problem of comparability of results between the two scales is bridged by Irwin & Whitehead’s (1991) common analytical framework. The results of this thesis supported the framework’s prediction that both scales are comparable. Therefore, the confidencerating scale was used for all studies within this thesis for interpretational clarity. Response bias data were not analysed in this thesis due to data artefacts created by correction methods for zero proportions in response categories. Methodologically, the construct validity of discriminability is influenced by the research design and procedures. Therefore, the following procedures were adopted to address weaknesses in previous studies. The one-interval confidence-rating task was used with a six-category confidence-rating scale and post-trial feedback. Based on a methodological study conducted within this thesis, the trial number was pragmatically reduced from 40 trials to 17 trials per stimulus intensity. This trial number reduction would not alter the mean and variance of the data sufficiently to influence the outcome of inferential statistical testing performed. Due to the novel use of the Quantitative Sensory Testing machine for the signal detection study procedures, accuracy and precision study on the machine was performed. This thesis found that the accuracy, repeatability and reproducibility of the machine in generating noxious thermal stimuli is excellent for the purposes of this thesis. Machine error is eliminated as a major source of variance for the thesis results. Theoretically, critics have challenged the construct validity of discriminability as an indicator of pain perception alteration. This thesis examined this issue in two separate contexts: 1) discriminability change as a correlate of local anaesthesia and, 2) discriminability as a correlate of psychological factors (depression and anxiety) in chronic low back pain (CLBP) sufferers. The results failed to establish the construct validity of discriminability for both contexts. However, the higher discriminability in CLBP sufferers compared to healthy individuals is in contrast to past research and warrant further investigation. This thesis addressed the construct validity issues through theoretical, methodological and interpretational modifications. A more robust analysis of the construct validity issue was facilitated. Caution is recommended on the use of discriminability as a pain perception measure until the construct validity issue has been satisfactorily resolved.
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Goldsby, Tamara L. "Intuition, openness to experience, and other personality correlates of pain-faking detection ability." Thesis, University of Surrey, 2008. http://epubs.surrey.ac.uk/844380/.

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This study seeks to examine the relationship between personality variables and pain perception ability. Specifically this thesis hypothesises that the Intuitive psychological type (rooted in Jung's theories of psychological type) will be effective at detecting faking of physical pain. Additionally, this thesis hypothesises that those scoring highly on Openness to Experience, as well as those rating as extraverts and those who rate high in emotional intelligence, will be more accurate at pain-faking detection than individuals who do not demonstrate these personality traits. It is also hypothesised that participants rating high in Machiavellianism will be accurate in detecting pain deception. In addition, it is speculated that a gender factor will exist in the ability to detect pain deception. In the first study, the Myers-Briggs Type Indicator (MBTI; Myers & McCaulley, 1985) and the NEO Five Factor Inventory (NEO-FFI; Costa & McCrae, 1992b) are used to measure participants' intuition level and openness to experience (as well as extraversion), respectively. In the second study, the Mayer Salovey Caruso Emotional Intelligence Test (MSCEIT; Mayer, Salovey & Caruso, 2002) and the Psychopathic Personality Inventory (PPI; Lilienfeld & Widows, 2005) are used to measure emotional intelligence and various psychopathy-related traits (including Machiavellianism), respectively. The stimuli presented are videotapes of facial expressions of pain, in which individuals display either genuine or faked physical pain. The first study's findings support the hypotheses that Intuitive and Open individuals are accurate at pain judgment, with Intuitive and Open females in particular displaying relationships with accuracy. Additionally, males categorised as 'Thinker' types, as well as those low in neuroticism predicted accuracy in pain judgment. However, the hypothesis that extraverted individuals would be more accurate at detection was largely unsupported. In the second study, the hypotheses that overall emotional intelligence and overall psychopathy would predict accuracy were generally unsupported. It was discovered, however, that particular subscales of the emotional intelligence measure such as the 'Changes' task were predictors of accuracy in pain judgment. The Machiavellianism subscale of the psychopathy measure was indeed a predictor of accuracy for male participants. In addition, other subscales of the psychopathy measure that predicted accuracy included Blame Externalisation, which had an inverse relationship with accuracy, as well as 'Fearlessness' in female participants. The significance of the study findings are discussed in terms of application to the realm of deception detection, as well as its relevance to the medical field in particular.
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Ogawa, Nozomi. "Detection of cellular redox status by transient receptor potential channels." 京都大学 (Kyoto University), 2016. http://hdl.handle.net/2433/215577.

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Sinha, Manas Kumar. "Ischemia modified albumin for the detection of myocardial ischaemia in acute coronary syndromes and chest pain syndromes." Thesis, St George's, University of London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406680.

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Doyle, Jason Emory. "Automatic Dynamic Tracking of Horse Head Facial Features in Video Using Image Processing Techniques." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/87582.

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The wellbeing of horses is very important to their care takers, trainers, veterinarians, and owners. This thesis describes the development of a non-invasive image processing technique that allows for automatic detection and tracking of horse head and ear motion, respectively, in videos or camera feed, both of which may provide indications of horse pain, stress, or well-being. The algorithm developed here can automatically detect and track head motion and ear motion, respectively, in videos of a standing horse. Results demonstrating the technique for nine different horses are presented, where the data from the algorithm is utilized to plot absolute motion vs. time, velocity vs. time, and acceleration vs. time for the head and ear motion, respectively, of a variety of horses and ponies. Two-dimensional plotting of x and y motion over time is also presented. Additionally, results of pilot work in eye detection in light colored horses is also presented. Detection of pain in horses is particularly difficult because they are prey animals and have mechanisms to disguise their pain, and these instincts may be particularly strong in the presence of an unknown human, such as a veterinarian. Current state-of-the art for detecting pain in horses primarily involves invasive methods, such as heart rate monitors around the body, drawing blood for cortisol levels, and pressing on painful areas to elicit a response, although some work has been done for humans to sort and score photographs subjectively in terms of a "horse grimace scale." The algorithms developed in this thesis are the first that the author is aware for exploiting proven image processing approaches from other applications for development of an automatic tool for detection and tracking of horse facial indicators. The algorithms were done in common open source programs Python and OpenCV, and standard image processing approaches including Canny Edge detection Hue, Saturation, Value color filtering, and contour tracking were utilized in algorithm development. The work in this thesis provides the foundational development of a non -invasive and automatic detection and tracking program for horse head and ear motion, including demonstration of the viability of this approach using videos of standing horses. This approach lays the groundwork for robust tool development for monitoring horses non-invasively and without the required presence of humans in such applications as post-operative monitoring, foaling, evaluation of performance horses in competition and/or training, as well as for providing data for research on animal welfare, among other scenarios.
MS
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Chew, Sien Wei. "Recognising facial expressions with noisy data." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/63523/1/Sien_Wei_Chew_Thesis.pdf.

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Techniques to improve the automated analysis of natural and spontaneous facial expressions have been developed. The outcome of the research has applications in several fields including national security (eg: expression invariant face recognition); education (eg: affect aware interfaces); mental and physical health (eg: depression and pain recognition).
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Books on the topic "Pain detection"

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Lehmann, Wilhelm. Kriteriumsbildung und Reizdiskrimination in der Schmerzwahrnehmung: Eine Signal-Detection-Analyse. Pfaffenweiler: Centaurus-Verlagsgesellschaft, 1987.

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BVA Animal Welfare Foundation Symposium (2nd 1985 [London]). Detection and relief of pain in animals: Proceedings of the 2nd Animal Welfare Foundation Symposium, held 16th April, 1985. Edited by Gibson T. E, Paterson D. A, and British Veterinary association. London: BVA Animal Welfare Foundation, 1985.

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The pain scale. Las Vegas, NV: Thomas & Mercer, 2012.

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Stahl, Jerry. Pain Killers. New York: HarperCollins, 2009.

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Pain killers. New York, NY: William Morrow, 2009.

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The pain detective: Every ache tells a story : understanding how stress and emotional hurt become chronic physical pain. Santa Barbara, Calif: Praeger, 2009.

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Pain management. New York: Knopf, 2001.

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Vachss, Andrew H. Pain Management. New York: Knopf Doubleday Publishing Group, 2001.

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Talton, Jon. The pain nurse. Scottsdale, AZ: Poisoned Pen Press, 2009.

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Bowen, Rhys. A royal pain. New York, N.Y: Berkley Pub. Group., 2009.

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Book chapters on the topic "Pain detection"

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Sullivan, Michael J. L., and Timothy H. Wideman. "Pain Catastrophizing and Fear of Movement: Detection and Intervention." In Clinical Pain Management, 207–14. Oxford, UK: Wiley-Blackwell, 2010. http://dx.doi.org/10.1002/9781444329711.ch25.

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Clark, W. Crawford. "Pain Measurement by Signal Detection Theory." In Sensory Systems: II, 89–91. Boston, MA: Birkhäuser Boston, 1988. http://dx.doi.org/10.1007/978-1-4684-6760-4_39.

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Subea, Oana, and George Suciu. "Facial Analysis Method for Pain Detection." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 167–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23976-3_17.

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Baloh, Robert W. "Nerves and the Detection and Perception of Pain." In Sciatica and Chronic Pain, 5–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93904-9_2.

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Lundtoft, Dennis H., Kamal Nasrollahi, Thomas B. Moeslund, and Sergio Escalera. "Spatiotemporal Facial Super-Pixels for Pain Detection." In Articulated Motion and Deformable Objects, 34–43. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41778-3_4.

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Sen, Kausik, Srejita Chakraborty, and Saurabh Pal. "Pain-Level Detection Using Heart Rate Variability." In Lecture Notes in Electrical Engineering, 185–99. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6855-8_15.

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Young, Justin M., and Stephen Thaddeus Connelly. "Pain Associated with Head and Neck Cancers." In Early Detection and Treatment of Head & Neck Cancers, 185–97. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69852-2_8.

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Shi, Heng, Belkacem Chikhaoui, and Shengrui Wang. "Tree-Based Models for Pain Detection from Biomedical Signals." In Lecture Notes in Computer Science, 183–95. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09593-1_14.

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AbstractFor medical treatments, pain is often measured by self-report. However, the current subjective pain assessment highly depends on the patient’s response and is therefore unreliable. In this paper, we propose a physiological-signals-based objective pain recognition method that can extract new features, which have never been discovered in pain detection, from electrodermal activity (EDA) and electrocardiogram (ECG) signals. To discriminate the absence and presence of pain, we establish four classification tasks and build four tree-based classifiers, including Random Forest, Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), and TabNet. The comparative experiments demonstrate that our method using the EDA and ECG features yields accurate classification results. Furthermore, the TabNet achieves a large accuracy improvement using our ECG features and a classification accuracy of 94.51% using the features selected from the fusion of the two signals.
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Young, Gerald. "An Instrument to Detect Pain Feigning: The Pain Feigning Detection Test (PFDT)." In Malingering, Feigning, and Response Bias in Psychiatric/ Psychological Injury, 471–509. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7899-3_19.

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Pikulkaew, Kornprom, Ekkarat Boonchieng, Waraporn Boonchieng, and Varin Chouvatut. "Pain Detection Using Deep Learning with Evaluation System." In Proceedings of Fifth International Congress on Information and Communication Technology, 426–35. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5859-7_42.

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Conference papers on the topic "Pain detection"

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Mansor, Muhammad Naufal, Syahryull Hi-Fi Syam Ahmad Jamil, Ahmad Kadri Junoh, Muhammad Nazri Rejab, Addzrull Hi-Fi Syam Ahmad Jamil, and Jamaluddin Ahmad. "Fast infant pain detection method." In 2012 International Conference on Computer and Communication Engineering (ICCCE). IEEE, 2012. http://dx.doi.org/10.1109/iccce.2012.6271350.

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Hammal, Zakia, and Jeffrey F. Cohn. "Automatic detection of pain intensity." In the 14th ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2388676.2388688.

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P, Balaji, Akash Selvin S, R. Shoba, and G. Lathaselvi. "Facial Pain Detection Using Deep Learning." In 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2023. http://dx.doi.org/10.1109/icses60034.2023.10465512.

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Blanco, D. A., and A. Diaz-Mendez. "Pain detection with EEG using Phase Indexes." In 2017 IEEE Healthcare Innovations and Point-of-Care Technologies (HI-POCT). IEEE, 2017. http://dx.doi.org/10.1109/hic.2017.8227581.

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Khan, Rizwan Ahmed, Alexandre Meyer, Hubert Konik, and Saida Bouakaz. "Pain detection through shape and appearance features." In 2013 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2013. http://dx.doi.org/10.1109/icme.2013.6607608.

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Mcwilliams, D. F., N. Frowd, L. Marshall, J. Stocks, A. Sarmanova, G. S. Fernandes, M. Hall, et al. "THU0519 Prediction of persistent knee pain by pressure pain detection thresholds: results from the knee pain in the community cohort (KPIC)." In Annual European Congress of Rheumatology, EULAR 2018, Amsterdam, 13–16 June 2018. BMJ Publishing Group Ltd and European League Against Rheumatism, 2018. http://dx.doi.org/10.1136/annrheumdis-2018-eular.4547.

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Semwal, Ashish, and Narendra D. Londhe. "Automated Pain Severity Detection Using Convolutional Neural Network." In 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS). IEEE, 2018. http://dx.doi.org/10.1109/ctems.2018.8769123.

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Kong, Youngsun, Hugo F. Posada-Quintero, and Ki H. Chon. "Pain Detection using a Smartphone in Real Time*." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9176077.

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Meawad, Fatma, Su-Yin Yang, and Fong Ling Loy. "Automatic detection of pain from spontaneous facial expressions." In ICMI '17: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3136755.3136794.

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Nerella, Subhash, Julie Cupka, Matthew Ruppert, Patrick Tighe, Azra Bihorac, and Parisa Rashidi. "Pain Action Unit Detection in Critically Ill Patients." In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2021. http://dx.doi.org/10.1109/compsac51774.2021.00094.

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Reports on the topic "Pain detection"

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Fitzpatrick, Gerald, and Richard Skaugset. Detection of Corrosion Under Paint Using Magneto-Optic Imaging. Fort Belvoir, VA: Defense Technical Information Center, February 1998. http://dx.doi.org/10.21236/adb233601.

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Arkko, J., and I. van Beijnum. Failure Detection and Locator Pair Exploration Protocol for IPv6 Multihoming. RFC Editor, June 2009. http://dx.doi.org/10.17487/rfc5534.

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Rimberg, Alexander. Ultrasensitive Charge Detection Using a Cavity Embedded Single Cooper Pair Transistor. Fort Belvoir, VA: Defense Technical Information Center, August 2014. http://dx.doi.org/10.21236/ada608157.

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McKnight, Mary E., and W. Eric Byrd. Screening procedures for detecting lead in existing paint films:. Gaithersburg, MD: National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.ir.89-4044.

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Rossiter, Walter J. Jr, Mark G. Vangel, Mary E. McKnight, and Gary Dewalt. Spot test kits for detecting lead in household paint:. Gaithersburg, MD: National Institute of Standards and Technology, 2000. http://dx.doi.org/10.6028/nist.ir.6398.

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Byrd, W. Eric, and Mary E. McKnight. Potential methods for measuring and detecting lead in existing paint films:. Gaithersburg, MD: National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.ir.89-4205.

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Glasscott, Matthew, Johanna Jernberg, Erik Alberts, and Lee Moores. Toward the electrochemical detection of 2,4-dinitroanisole (DNAN) and pentaerythritol tetranitrate (PETN). Engineer Research and Development Center (U.S.), March 2022. http://dx.doi.org/10.21079/11681/43826.

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Abstract:
Analytical methods to rapidly detect explosive compounds with high precision are paramount for applications ranging from national security to environmental remediation. This report demonstrates two proof-of-concept electroanalytical methods for the quantification of 2,4-dinitroanisol (DNAN) and pentaerythritol tetranitrate (PETN). For the first time, DNAN reduction was analyzed and compared at a bare graphitic carbon electrode, a polyaniline-modified (PANI) electrode, and a molecularly imprinted polymer (MIP) electrode utilizing PANI to explore the effect of surface-area and preconcentration affinity on the analytical response. Since some explosive compounds such as PETN are not appreciably soluble in water (<10 μg/L), necessitating a different solvent system to permit direct detection via electrochemical reduction. A 1,2-dichloroethane system was explored as a possibility by generating a liquid-liquid extraction-based sensor exploiting the immiscibility of 1,2-dichloroethane and water. The reduction process was explored using a scan rate analysis to extract a diffusion coefficient of 6.67 x 10⁻⁶ cm/s, in agreement with literature values for similarly structured nitrate esters. Once further refined, these techniques may be extended to other explosives and combined with portable electrochemical hardware to bring real-time chemical information to soldiers and citizens alike.
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Tecker-Shreyber, Irina. Radiative Top-Quark Pair Production with the CDF Detector at the Tevatron. Office of Scientific and Technical Information (OSTI), January 2009. http://dx.doi.org/10.2172/1343952.

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Munger, Charles T. Proposal for the Detection of Relativistic Anti-Hydrogen Atoms Produced by Pair Production with Positron Capture. Office of Scientific and Technical Information (OSTI), July 2003. http://dx.doi.org/10.2172/813309.

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Wang, Zhongmin. Search for chargino-neutralino pair production with the D0 detector at the Tevatron. Office of Scientific and Technical Information (OSTI), May 2004. http://dx.doi.org/10.2172/875576.

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