A1 Refereed original research article in a scientific journal

Acute pain intensity monitoring with the classification of multiple physiological parameters




AuthorsMingzhe Jiang, Riitta Mieronkoski, Elise Syrjälä, Arman Anzanpour, Virpi Terävä, Amir M. Rahmani, Sanna Salanterä, Riku Aantaa, Nora Hagelberg, Pasi Liljeberg

PublisherSpringer Netherlands

Publication year2019

JournalJournal of Clinical Monitoring and Computing

Journal name in sourceJournal of Clinical Monitoring and Computing

Volume33

Issue3

First page 493

Last page507

Number of pages15

ISSN1387-1307

eISSN1573-2614

DOIhttps://doi.org/10.1007/s10877-018-0174-8

Web address https://link.springer.com/article/10.1007/s10877-018-0174-8

Self-archived copy’s web addresshttps://research.utu.fi/converis/portal/detail/Publication/34271864


Abstract

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.


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Last updated on 2024-26-11 at 12:07