Acute pain intensity monitoring with the classification of multiple physiological parameters.
Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On th...
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 33; no. 3; pp. 493 - 508 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136223993&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136223993 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Jun2019 vid: 33 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136223993 136223993 NLM29946994 10.1007/s10877-018-0174-8 NLM29946994 136223993 ppf: 493 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Acute pain intensity monitoring with the classification of multiple physiological parameters. aug: au: Jiang, Mingzhe Mieronkoski, Riitta Syrjälä, Elise Anzanpour, Arman Terävä, Virpi Rahmani, Amir M. Salanterä, Sanna Aantaa, Riku Hagelberg, Nora Liljeberg, Pasi affil: Department of Future Technologies, University of Turku, Turku, Finland sug: subj: Critical Illness Neural Networks (Computer) Pain Measurement Methods Heart Rate Monitoring, Physiologic Methods Pain Diagnosis Male Pharmacokinetics Skin Physiology Research Subjects Female Heat Reproducibility of Results ROC Curve Respiration Adult Young Adult Electromyography Arthritis Impact Measurement Scales Adult: 19-44 years Male Female ab: Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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