Electroencephalogram-Based Pain Classification Using Artificial Neural Networks

Kaur, Manpreet; Prakash, Neelam Rup; Kalra, Pramila; Puri, Goverdhan Dutt

doi:10.1080/03772063.2019.1702903

Cited by 3 publications

(13 citation statements)

References 25 publications

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“…They also observed a significant correlation between the pain intensity reported during the CPT and the cingulate activity in the beta2, beta3, and gamma bands. 45 Similar findings were observed by Kaur et al 46 using 4 unipolar recordings (Fp1, Fp2, P3, and P4). They observed a relative increase in delta power, while a relative decrease in alpha power in healthy young adults during a cold water immersion.…”

Section: Cerebral Features Of Thermal Cold Stimulisupporting

confidence: 80%

“…They observed a relative increase in delta power, while a relative decrease in alpha power in healthy young adults during a cold water immersion. 46 Recently, Navid et al 47 investigated central processing in adults with acute pain using standardized low-resolution brain electromagnetic tomography on a 61-channel EEG, and they observed similar results as Hansen et al, 45 which was an increase in brain activity in the delta (1-4 Hz), theta (4-8 Hz), and beta (12-32 Hz) frequency bands following an 80 s hand immersion in 2 °C. However, an increase in brain activity in the alpha (8-12 Hz) frequency band was also observed.…”

Section: Cerebral Features Of Thermal Cold Stimulimentioning

confidence: 67%

“…However, research groups have turned to mainly use network dynamics in their feature extraction. Kaur et al 46 used a combination of backpropagation algorithms and error functions to determine an optimal network configuration of a multilayer perception neural network for pain detection and were able to obtain accuracies ranging from 87.53% to 93.34%. 46 More recently, research groups turned to investigate the time-frequency representation of the EEG signals in their machine learning models.…”

Section: Cerebral Features Of Thermal Cold Stimulimentioning

confidence: 99%

“…Kaur et al 46 used a combination of backpropagation algorithms and error functions to determine an optimal network configuration of a multilayer perception neural network for pain detection and were able to obtain accuracies ranging from 87.53% to 93.34%. 46 More recently, research groups turned to investigate the time-frequency representation of the EEG signals in their machine learning models. Alazrai et al 48 displayed two approached to detect EEG-based pain: Choi-Williams distribution analysis 48 and discrete wavelet transformation.…”

Section: Cerebral Features Of Thermal Cold Stimulimentioning

confidence: 99%

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Clinical use of Electroencephalography in the Assessment of Acute Thermal Pain: A Narrative Review Based on Articles From 2009 to 2019

et al. 2021

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Nowadays, no practical system has successfully been able to decode and predict pain in clinical settings. The inability of some patients to verbally express their pain creates the need for a tool that could objectively assess pain in these individuals. Neuroimaging techniques combined with machine learning are seen as possible candidates for the identification of pain biomarkers. This review aimed to address the potential use of electroencephalographic features as predictors of acute experimental pain. Twenty-six studies using only thermal stimulations were identified using a PubMed and Scopus search. Combinations of the following terms were used: “EEG,” “Electroencephalography,” “Acute,” “Pain,” “Tonic,” “Noxious,” “Thermal,” “Stimulation,” “Brain,” “Activity,” “Cold,” “Subjective,” and “Perception.” Results revealed that contact-heat-evoked potentials have been widely recorded over central areas during noxious heat stimulations. Furthermore, a decrease in alpha power over central regions was revealed, as well as increased theta and gamma powers over frontal areas. Gamma and theta rhythms were associated with connectivity between sensory and affective regions involved in pain processing. A machine learning analysis revealed that the gamma band is a predominant predictor of acute thermal pain. This review also addressed the need of supplementing current spectral features with techniques that allow the investigation of network dynamics.

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Section: Cerebral Features Of Thermal Cold Stimulisupporting

confidence: 80%

Section: Cerebral Features Of Thermal Cold Stimulimentioning

confidence: 67%

Section: Cerebral Features Of Thermal Cold Stimulimentioning

confidence: 99%

Section: Cerebral Features Of Thermal Cold Stimulimentioning

confidence: 99%

See 2 more Smart Citations

Clinical use of Electroencephalography in the Assessment of Acute Thermal Pain: A Narrative Review Based on Articles From 2009 to 2019

et al. 2021

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“…The preprocessed EEG was segmented into 10 s, nonoverlapping epochs and analyzed in Matlab using custom scripts. All features described below were calculated at four frequency bands: 1) delta (1-4 Hz), 2) theta (4-8 Hz), 3) alpha (8-13 Hz), and 4) beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Seven classes of EEG features were calculated for each 10-second epoch: spectral power, peak frequency, permutation entropy (PE), weighted phase lag index (wPLI), directed phase lag index (dPLI), and graph theory features (path length, clustering coefficient, smallworld architecture, modularity, and node strength).…”

Section: Eeg Feature Extractionmentioning

confidence: 99%

Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain

et al. 2022

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ObjectiveWe assessed the potential of using EEG to detect cold thermal pain in adolescents with and without chronic musculoskeletal pain.MethodsThirty-nine healthy controls (15.2 ± 2.1 years, 18 females) and 121 chronic pain participants (15.0 ± 2.0 years, 100 females, 85 experiencing pain ≥12-months) had 19-channel EEG recorded at rest and throughout a cold-pressor task (CPT). Permutation entropy, directed phase lag index, peak frequency, and binary graph theory features were calculated across 10-second EEG epochs (Healthy: 292 baseline / 273 CPT epochs; Pain: 1039 baseline / 755 CPT epochs). Support vector machine (SVM) and logistic regression models were trained to classify between baseline and CPT conditions separately for control and pain participants.ResultsSVM models significantly distinguished between baseline and CPT conditions in chronic pain (75.2% accuracy, 95% CI: 71.4%–77.1%; p < 0.0001) and control (74.8% accuracy, 95% CI: 66.3%–77.6%; p < 0.0001) participants. Logistic regression models performed similar to the SVM (Pain: 75.8% accuracy, 95% CI: 69.5%–76.6%, p < 0.0001; Controls: 72.0% accuracy, 95% CI: 64.5%–78.5%, p < 0.0001). Permutation entropy features in the theta frequency band were the largest contributor to model accuracy for both groups.ConclusionsOur results demonstrate that subjective pain experiences can accurately be detected from electrophysiological data, and represent the first step towards the development of a point-of-care system to detect pain in the absence of self-report.

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