Pain Level Modeling of Intensive Care Unit patients with Machine Learning Methods: An Effective Congeneric Clustering-based Approach

Fang, Ruijie; Zhang, Ruoyu; Hosseini, Sayed Mohammad; Faghih, Mahya; Rafatirad, Soheil; Rafatirad, Setareh; Homayoun, Houman

doi:10.1145/3524086.3524100

Cited by 11 publications

(1 citation statement)

References 18 publications

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“…Additionally, skin temperature and respiratory features have been used as metrics to evaluate pain. For example, local skin temperature (e.g., fingertip) decreases after experiencing painful stimuli ( 12 , 24 ); a decrease in respiration rate (RR), which measures the number of breaths per minute, has been reported in response to pain ( 24 , 25 ). Since each of these physiological measures has its constraints and advantages, integrating them can improve pain assessment.…”

Section: Introductionmentioning

confidence: 99%

Comparing multimodal physiological responses to social and physical pain in healthy participants

Jang,

Eum,

Yoon

et al. 2024

Front. Public Health

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BackgroundPrevious physiology-driven pain studies focused on examining the presence or intensity of physical pain. However, people experience various types of pain, including social pain, which induces negative mood; emotional distress; and neural activities associated with physical pain. In particular, comparison of autonomic nervous system (ANS) responses between social and physical pain in healthy adults has not been well demonstrated.MethodsWe explored the ANS responses induced by two types of pain—social pain, associated with a loss of social ties; and physical pain, caused by a pressure cuff—based on multimodal physiological signals. Seventy-three healthy individuals (46 women; mean age = 20.67 ± 3.27 years) participated. Behavioral responses were assessed to determine their sensitivity to pain stimuli. Electrocardiogram, electrodermal activity, photoplethysmogram, respiration, and finger temperature (FT) were measured, and 12 features were extracted from these signals.ResultsSocial pain induced increased heart rate (HR) and skin conductance (SC) and decreased blood volume pulse (BVP), pulse transit time (PTT), respiration rate (RR), and FT, suggesting a heterogeneous pattern of sympathetic–parasympathetic coactivation. Moreover, physical pain induced increased heart rate variability (HRV) and SC, decreased BVP and PTT, and resulted in no change in FT, indicating sympathetic-adrenal-medullary activation and peripheral vasoconstriction.ConclusionThese results suggest that changes in HR, HRV indices, RR, and FT can serve as markers for differentiating physiological responses to social and physical pain stimuli.

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Section: Introductionmentioning

confidence: 99%

Comparing multimodal physiological responses to social and physical pain in healthy participants

Jang,

Eum,

Yoon

et al. 2024

Front. Public Health

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An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition

Gouverneur,

Badura,

et al. 2024

Sci Data

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Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses. Although machine learning models have shown promise in improving the current gold standard of pain monitoring (self-reports) only a handful of datasets are freely accessible to researchers. This paper presents the PainMonit Dataset for automated pain detection using physiological data. The dataset consists of two parts, as pain can be perceived differently depending on its underlying cause. (1) Pain was triggered by heat stimuli in an experimental study during which nine physiological sensor modalities (BVP, 2×EDA, skin temperature, ECG, EMG, IBI, HR, respiration) were recorded from 55 healthy subjects. (2) Eight modalities (2×BVP, 2×EDA, EMG, skin temperature, respiration, grip) were recorded from 49 participants to assess their pain during a physiotherapy session.

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A systematic review of neurophysiological sensing for the assessment of acute pain

et al. 2023

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Pain is a complex and personal experience that presents diverse measurement challenges. Different sensing technologies can be used as a surrogate measure of pain to overcome these challenges. The objective of this review is to summarise and synthesise the published literature to: (a) identify relevant non-invasive physiological sensing technologies that can be used for the assessment of human pain, (b) describe the analytical tools used in artificial intelligence (AI) to decode pain data collected from sensing technologies, and (c) describe the main implications in the application of these technologies. A literature search was conducted in July 2022 to query PubMed, Web of Sciences, and Scopus. Papers published between January 2013 and July 2022 are considered. Forty-eight studies are included in this literature review. Two main sensing technologies (neurological and physiological) are identified in the literature. The sensing technologies and their modality (unimodal or multimodal) are presented. The literature provided numerous examples of how different analytical tools in AI have been applied to decode pain. This review identifies different non-invasive sensing technologies, their analytical tools, and the implications for their use. There are significant opportunities to leverage multimodal sensing and deep learning to improve accuracy of pain monitoring systems. This review also identifies the need for analyses and datasets that explore the inclusion of neural and physiological information together. Finally, challenges and opportunities for designing better systems for pain assessment are also presented.

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Pain Level Modeling of Intensive Care Unit patients with Machine Learning Methods: An Effective Congeneric Clustering-based Approach

Cited by 11 publications

References 18 publications

Comparing multimodal physiological responses to social and physical pain in healthy participants

Comparing multimodal physiological responses to social and physical pain in healthy participants

An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition

A systematic review of neurophysiological sensing for the assessment of acute pain

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