Pain phenotypes classified by machine learning using electroencephalography features
Joshua Levitt, Muhammad Muzzammil Edhi, Ryan Thorpe, Jason Leung, Mai Michishita, Suguru Koyama, Satoru Yoshikawa, Keith Scarfo, Alexios G. Carayannopoulos, Wendy Gu, Kyle Srivastava, Bryan A. Clark, Rosana Esteller, David A. Borton, Stephanie R. Jones, Carl Y. Saab
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.