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Normalization of Pain-Evoked Neural Responses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction.

Bai Y, Huang G, Tu Y, Tan A, Hung YS, Zhang Z - Front Comput Neurosci (2016)

Bottom Line: Recent studies have shown that pain-evoked electroencephalography (EEG) responses could be used as a readout of perceived pain intensity.In this study, to reduce the inter-individual variability in pain-evoked EEG and to improve the accuracy of cross-individual pain prediction, we examined the relationship between pain-evoked EEG, spontaneous EEG, and pain perception on a pain EEG dataset, where a large number of laser pulses (>100) with a wide energy range were delivered.Motivated by our finding that an individual's pain-evoked EEG responses is significantly correlated with his/her spontaneous EEG in terms of magnitude, we proposed a normalization method for pain-evoked EEG responses using one's spontaneous EEG to reduce the inter-individual variability.

View Article: PubMed Central - PubMed

Affiliation: School of Chemical and Biomedical Engineering, Nanyang Technological UniversitySingapore, Singapore; School of Data and Computer Science, Sun Yat-Sen UniversityLouvain, Belgium.

ABSTRACT
An effective physiological pain assessment method that complements the gold standard of self-report is highly desired in pain clinical research and practice. Recent studies have shown that pain-evoked electroencephalography (EEG) responses could be used as a readout of perceived pain intensity. Existing EEG-based pain assessment is normally achieved by cross-individual prediction (i.e., to train a prediction model from a group of individuals and to apply the model on a new individual), so its performance is seriously hampered by the substantial inter-individual variability in pain-evoked EEG responses. In this study, to reduce the inter-individual variability in pain-evoked EEG and to improve the accuracy of cross-individual pain prediction, we examined the relationship between pain-evoked EEG, spontaneous EEG, and pain perception on a pain EEG dataset, where a large number of laser pulses (>100) with a wide energy range were delivered. Motivated by our finding that an individual's pain-evoked EEG responses is significantly correlated with his/her spontaneous EEG in terms of magnitude, we proposed a normalization method for pain-evoked EEG responses using one's spontaneous EEG to reduce the inter-individual variability. In addition, a nonlinear relationship between the level of pain perception and pain-evoked EEG responses was obtained, which inspired us to further develop a new two-stage pain prediction strategy, a binary classification of low-pain and high-pain trials followed by a continuous prediction for high-pain trials only, both of which used spontaneous-EEG-normalized magnitudes of evoked EEG responses as features. Results show that the proposed normalization strategy can effectively reduce the inter-individual variability in pain-evoked responses, and the two-stage pain prediction method can lead to a higher prediction accuracy.

No MeSH data available.


Related in: MedlinePlus

Correlation between the mean and SD of RMSS and RMSP at levels of (A) NRS ≤ 4, and (B) NRS > 4. Red dots represent the mean or SD of RMSS and RMSP, which are averaged across all trials at each pain intensity level for each participant. Gray lines represent the best linear fit.
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Figure 1: Correlation between the mean and SD of RMSS and RMSP at levels of (A) NRS ≤ 4, and (B) NRS > 4. Red dots represent the mean or SD of RMSS and RMSP, which are averaged across all trials at each pain intensity level for each participant. Gray lines represent the best linear fit.

Mentions: For each participant, the mean and SD of RMSS and RMSP at each pain intensity level were calculated. Since NRS > 8 was not available for some participants, we use a combined level of “NRS > 8” to denote all trials with an NRS > 8. It can be clearly seen from Figure 1 and Table 1 that, a significant correlation (p ≤ 0.007) between the mean values of RMSS and RMSP was obtained at each intensity level of pain perception. In addition, a significant correlation between the SD values of RMSS and RMSP was also obtained (p ≤ 0.02) for overall intensity level of low-pain (NRS ≤ 4) and high-pain (NRS > 4), though some individual intensity level is not significant (such as intensity level at 2–3, 4–5, 6–7, and 8–10). To conclude, the distributions of RMSS and RMSP are highly correlated, which verifies our hypothesis that magnitudes of pEEG are highly correlated with the magnitude of sEEG. This observation supports the idea that RMSS could serve as an individual scale to normalize his/her RMSP to reduce inter-individual variability of pain-related features in pain classification and prediction models.


Normalization of Pain-Evoked Neural Responses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction.

Bai Y, Huang G, Tu Y, Tan A, Hung YS, Zhang Z - Front Comput Neurosci (2016)

Correlation between the mean and SD of RMSS and RMSP at levels of (A) NRS ≤ 4, and (B) NRS > 4. Red dots represent the mean or SD of RMSS and RMSP, which are averaged across all trials at each pain intensity level for each participant. Gray lines represent the best linear fit.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4829613&req=5

Figure 1: Correlation between the mean and SD of RMSS and RMSP at levels of (A) NRS ≤ 4, and (B) NRS > 4. Red dots represent the mean or SD of RMSS and RMSP, which are averaged across all trials at each pain intensity level for each participant. Gray lines represent the best linear fit.
Mentions: For each participant, the mean and SD of RMSS and RMSP at each pain intensity level were calculated. Since NRS > 8 was not available for some participants, we use a combined level of “NRS > 8” to denote all trials with an NRS > 8. It can be clearly seen from Figure 1 and Table 1 that, a significant correlation (p ≤ 0.007) between the mean values of RMSS and RMSP was obtained at each intensity level of pain perception. In addition, a significant correlation between the SD values of RMSS and RMSP was also obtained (p ≤ 0.02) for overall intensity level of low-pain (NRS ≤ 4) and high-pain (NRS > 4), though some individual intensity level is not significant (such as intensity level at 2–3, 4–5, 6–7, and 8–10). To conclude, the distributions of RMSS and RMSP are highly correlated, which verifies our hypothesis that magnitudes of pEEG are highly correlated with the magnitude of sEEG. This observation supports the idea that RMSS could serve as an individual scale to normalize his/her RMSP to reduce inter-individual variability of pain-related features in pain classification and prediction models.

Bottom Line: Recent studies have shown that pain-evoked electroencephalography (EEG) responses could be used as a readout of perceived pain intensity.In this study, to reduce the inter-individual variability in pain-evoked EEG and to improve the accuracy of cross-individual pain prediction, we examined the relationship between pain-evoked EEG, spontaneous EEG, and pain perception on a pain EEG dataset, where a large number of laser pulses (>100) with a wide energy range were delivered.Motivated by our finding that an individual's pain-evoked EEG responses is significantly correlated with his/her spontaneous EEG in terms of magnitude, we proposed a normalization method for pain-evoked EEG responses using one's spontaneous EEG to reduce the inter-individual variability.

View Article: PubMed Central - PubMed

Affiliation: School of Chemical and Biomedical Engineering, Nanyang Technological UniversitySingapore, Singapore; School of Data and Computer Science, Sun Yat-Sen UniversityLouvain, Belgium.

ABSTRACT
An effective physiological pain assessment method that complements the gold standard of self-report is highly desired in pain clinical research and practice. Recent studies have shown that pain-evoked electroencephalography (EEG) responses could be used as a readout of perceived pain intensity. Existing EEG-based pain assessment is normally achieved by cross-individual prediction (i.e., to train a prediction model from a group of individuals and to apply the model on a new individual), so its performance is seriously hampered by the substantial inter-individual variability in pain-evoked EEG responses. In this study, to reduce the inter-individual variability in pain-evoked EEG and to improve the accuracy of cross-individual pain prediction, we examined the relationship between pain-evoked EEG, spontaneous EEG, and pain perception on a pain EEG dataset, where a large number of laser pulses (>100) with a wide energy range were delivered. Motivated by our finding that an individual's pain-evoked EEG responses is significantly correlated with his/her spontaneous EEG in terms of magnitude, we proposed a normalization method for pain-evoked EEG responses using one's spontaneous EEG to reduce the inter-individual variability. In addition, a nonlinear relationship between the level of pain perception and pain-evoked EEG responses was obtained, which inspired us to further develop a new two-stage pain prediction strategy, a binary classification of low-pain and high-pain trials followed by a continuous prediction for high-pain trials only, both of which used spontaneous-EEG-normalized magnitudes of evoked EEG responses as features. Results show that the proposed normalization strategy can effectively reduce the inter-individual variability in pain-evoked responses, and the two-stage pain prediction method can lead to a higher prediction accuracy.

No MeSH data available.


Related in: MedlinePlus