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Pain phenotypes classified by machine learning using electroencephalography features.

Abstract：

:Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an implanted spinal cord stimulator. Analysis of power spectral density, coherence, and phase-amplitude coupling using conventional statistics showed that there were no significant differences between the radiculopathy and control groups after correcting for multiple comparisons. However, analysis of transient spectral events showed that there were differences between these two groups in terms of the number, power, and frequency-span of events in a low gamma band. Finally, we trained a binary support vector machine to classify radiculopathy versus healthy subjects, as well as a 3-way classifier for subjects in the 3 groups. Both classifiers performed significantly better than chance, indicating that EEG features contain relevant information pertaining to sensory states, and may be used to help distinguish between pain states when other clinical signs are inconclusive.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Levitt J,Edhi MM,Thorpe RV,Leung JW,Michishita M,Koyama S,Yoshikawa S,Scarfo KA,Carayannopoulos AG,Gu W,Srivastava KH,Clark BA,Esteller R,Borton DA,Jones SR,Saab CY

doi

10.1016/j.neuroimage.2020.117256

subject

Has Abstract

pub_date

2020-12-01 00:00:00

pages

117256

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(20)30742-4

journal_volume

223

pub_type

杂志文章

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