Artificial neural networks applied to somatosensory evoked potentials for migraine classification.

backgroundFinding a biomarker to diagnose migraine remains a significant challenge in the headache field. Migraine patients exhibit dynamic and recurrent alterations in the brainstem-thalamo-cortical loop, including reduced thalamocortical activity and abnormal habituation during the interictal phase. Although these insights into migraine pathophysiology have been valuable, they are not currently used in clinical practice. This study aims to evaluate the potential of Artificial Neural Networks (ANNs) in distinguishing migraine patients from healthy individuals using neurophysiological recordings.

methodsWe recorded Somatosensory Evoked Potentials (SSEPs) to gather electrophysiological data from low- and high-frequency signal bands in 177 participants, comprising 91 migraine patients (MO) during their interictal period and 86 healthy volunteers (HV). Eleven neurophysiological variables were analyzed, and Principal Component Analysis (PCA) and Forward Feature Selection (FFS) techniques were independently employed to identify relevant variables, refine the feature space, and enhance model interpretability. The ANNs were then trained independently with the features derived from the PCA and FFS to delineate the relationship between electrophysiological inputs and the diagnostic outcome.

resultsBoth models demonstrated robust performance, achieving over 68% in all the performance metrics (accuracy, sensitivity, specificity, and F1 scores). The classification model trained with FFS-derived features performed better than the model trained with PCA results in distinguishing patients with MO from HV. The model trained with FFS-derived features achieved a median accuracy of 72.8% and an area under the curve (AUC) of 0.79, while the model trained with PCA results showed a median accuracy of 68.9% and an AUC of 0.75.

conclusionOur findings suggest that ANNs trained with SSEP-derived variables hold promise as a noninvasive tool for migraine classification, offering potential for clinical application and deeper insights into migraine diagnostics.