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Somatosensory Event-related Potentials from Orofacial Skin Stretch Stimulation

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Published on: December 18, 2015

Migraine classification using somatosensory evoked potentials.

Bingzhao Zhu^1,2, Gianluca Coppola³, Mahsa Shoaran¹

¹1 School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.

Cephalalgia : an International Journal of Headache

|March 28, 2019

Summary

This summary is machine-generated.

Machine learning accurately classifies migraine states using non-invasive somatosensory evoked potentials (SEPs). This approach reliably distinguishes migraineurs in ictal and interictal phases from healthy individuals.

Keywords:

Migraine attack early detection electrophysiological recording feature selection machine learning

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A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Area of Science:

Neuroscience
Biomedical Engineering
Computational Biology

Background:

Migraine diagnosis and treatment can be improved by automatic detection using electrophysiological recordings.
Migraineurs exhibit impaired cortical information processing, specifically a deficit in habituation, leading to altered somatosensory evoked potentials (SEPs).

Purpose of the Study:

To develop and validate a machine learning approach for classifying migraine states (ictal and interictal) versus healthy controls using non-invasive SEPs.
To identify reliable SEP-based biomarkers for migraine classification.

Main Methods:

Collected SEPs from 42 migraine patients (29 interictal, 13 ictal) and 15 healthy controls.
Applied various machine learning algorithms (Random Forest, XGBoost, SVM, KNN, MLP, LDA, Logistic Regression) using time and frequency domain SEP features.
Utilized feature selection to optimize the classification model and identify key biomarkers.

Main Results:

Initial classification accuracies ranged from 51.2% to 72.4% for healthy-ictal-interictal tasks.
After model and feature selection, high accuracies were achieved: 89.7% (healthy-ictal), 88.7% (healthy-interictal), 80.2% (ictal-interictal), and 73.3% (healthy-ictal-interictal).

Conclusions:

Somatosensory evoked potentials (SEPs) show significant potential as reliable biomarkers for migraine classification.
The developed non-invasive SEP-based machine learning system can effectively differentiate migraine patients in ictal and interictal states from healthy controls.