Pain phenotypes classified by machine learning using electroencephalography features

• Published in: NeuroImage (Orlando, Fla.) Vol. 223; p. 117256
• Main Authors: Levitt, Joshua, Edhi, Muhammad M., Thorpe, Ryan V., Leung, Jason W., Michishita, Mai, Koyama, Suguru, Yoshikawa, Satoru, Scarfo, Keith A., Carayannopoulos, Alexios G., Gu, Wendy, Srivastava, Kyle H., Clark, Bryan A., Esteller, Rosana, Borton, David A., Jones, Stephanie R., Saab, Carl Y.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.12.2020; Elsevier Limited; Elsevier
• Subjects: Adult; Aged; Aged, 80 and over; Brain Waves; Chronic pain; EEG; Electrical stimuli; Electroencephalography; Female; Human subjects; Humans; Hypotheses; Learning algorithms; Lumbosacral Region - physiopathology; Machine Learning; Male; Middle Aged; Neural networks; Pain; Pain - classification; Pain - diagnosis; Pain - physiopathology; Phenotypes; Radiculopathy - complications; Radiculopathy - diagnosis; Radiculopathy - physiopathology; Signal Processing, Computer-Assisted; Spinal cord; Spinal Diseases - complications; Spinal Diseases - diagnosis; Spinal Diseases - physiopathology; Statistical analysis; Support vector machines
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.117256

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.