Pain Recognition from Finger Plethysmography using Neural Networks

Piartli, Povilas; Rapalis, Andrius; Kaniušas, Eugenijus; Jankauskaitė, Lina; Marozas, Vaidotas

doi:10.1109/biomdlore49470.2021.9594510

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…In current clinical settings, patients typically self-report their pain level using the Verbal Rating Scale (VRS), Visual Analogue Scale (VAS), and Numerical Rating Scale (NRS) [19][20][21]. These self-reported pain measurements are subjective and often impractical when patients are not in an alert state or unable to communicate their pain level [22,23]. Infants, toddlers, and adults with communication or cognitive deficits may be unable to convey their pain levels verbally [24].…”

Section: Pain Assessmentmentioning

confidence: 99%

Analysis of pain research literature through keyword Co-occurrence networks

Ozek,

Lu,

Pouromran

et al. 2023

PLOS Digit Health

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Pain is a significant public health problem as the number of individuals with a history of pain globally keeps growing. In response, many synergistic research areas have been coming together to address pain-related issues. This work reviews and analyzes a vast body of pain-related literature using the keyword co-occurrence network (KCN) methodology. In this method, a set of KCNs is constructed by treating keywords as nodes and the co-occurrence of keywords as links between the nodes. Since keywords represent the knowledge components of research articles, analysis of KCNs will reveal the knowledge structure and research trends in the literature. This study extracted and analyzed keywords from 264,560 pain-related research articles indexed in IEEE, PubMed, Engineering Village, and Web of Science published between 2002 and 2021. We observed rapid growth in pain literature in the last two decades: the number of articles has grown nearly threefold, and the number of keywords has grown by a factor of 7. We identified emerging and declining research trends in sensors/methods, biomedical, and treatment tracks. We also extracted the most frequently co-occurring keyword pairs and clusters to help researchers recognize the synergies among different pain-related topics.

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