EEG frequency band analysis in chronic neuropathic pain: A linear and nonlinear approach to classify pain severity
•Chronic neuropathic pain has a considerable gap in characterization.•Neuropathic pain has mostly been analyzed in EEG through linear methodologies.•The central nervous system undergoing pain is a dynamic and unpredictable system.•We compare linear and nonlinear methodologies to classify pain.•Neuro...
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Veröffentlicht in: | Computer methods and programs in biomedicine 2023-03, Vol.230, p.107349-107349, Article 107349 |
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Sprache: | eng |
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Zusammenfassung: | •Chronic neuropathic pain has a considerable gap in characterization.•Neuropathic pain has mostly been analyzed in EEG through linear methodologies.•The central nervous system undergoing pain is a dynamic and unpredictable system.•We compare linear and nonlinear methodologies to classify pain.•Neuropathic pain severities are more significantly differentiated with approximate entropy as compared to absolute band power in the different neuronal frequency bands.
Chronic neuropathic pain (NP) is a chronic pain condition that severely impacts a patient's life. Pain management has proved to be inefficient due to a lack of a simple clinical tool that may identify and monitor NP. A low-cost, noninvasive tool that provides relevant information on NP is the electroencephalogram (EEG). However, the commonly used linear EEG features have proved to be limited in characterizing NP pathophysiology. This study sought to determine whether nonlinear EEG features such as approximate entropy (ApEn) would better differentiate pain severity than absolute band power.
A non-parametric statistical approach based on the Brief Pain Inventory (BPI), along with linear and nonlinear EEG features, is proposed in this study. For this purpose, thirty-six chronic NP patients were recruited, and 22 channels were registered. Additionally, a control database of 13 participants with no NP was used as a reference, where 19 channels were registered. For both groups, EEG was recorded for 10 min in a resting state: 5 min with eyes open (EO) and 5 min with eyes closed (EC). Absolute band power and ApEn EEG features in the five clinical frequency bands (delta, theta, alpha, beta, and gamma) were estimated for all channels in both groups. As a result, 220-dimensional and 190-dimensional feature vectors were obtained for experimental and control classes respectively. For the experimental class, NP patients were grouped according to their BPI evaluation in three groups: low, moderate, and high pain. Finally, feature vectors were compared between groups using Kruskal Wallis and post-hoc Dunn's tests.
ApEn revealed significant statistical difference (p |
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ISSN: | 0169-2607 1872-7565 |
DOI: | 10.1016/j.cmpb.2023.107349 |