Classification of chronic pain and spinal cord stimulation response using machine learning in magnetoencephalography data

This study achieved a promising 76% accuracy in classifying patients with chronic pain and pain-free controls using the theta power or alpha power ratio. However, this model's output poorly correlated with pain scores of patients with SCS. A larger variety of input features and outcome parameters is recommended.

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Publication Details

Journal
PLoS ONE
Published
2025-12-05
DOI
https://doi.org/10.1371/journal.pone.0337726
Primary Topic
Pain Mechanisms and Treatments
Type
article
Field-Weighted Citation Impact
0.00
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article

Classification of chronic pain and spinal cord stimulation response using machine learning in magnetoencephalography data

Frank Huygen, Bart Witjes, Martijn P. A. Starmans, Cecile C. de Vos
PLoS ONE
Pain Mechanisms and Treatments
article

Classification of chronic pain and spinal cord stimulation response using machine learning in magnetoencephalography data

Frank Huygen, Bart Witjes, Martijn P. A. Starmans, Cecile C. de Vos
article en

Abstract

This study achieved a promising 76% accuracy in classifying patients with chronic pain and pain-free controls using the theta power or alpha power ratio. However, this model's output poorly correlated with pain scores of patients with SCS. A larger variety of input features and outcome parameters is recommended.

PLoS ONEVol. 20(12)
Erasmus MC (NL)
Openalex Percentile: Top 52%
Pain Mechanisms and Treatments
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