Identification of Migraine Subtypes Using Functional Near-Infrared Spectroscopy Data: A Domain-Based Feature Extraction

Abstract

Migraine diagnosis relies on subjective patient reports and International Headache Society guidelines, leading to misdiagnoses. In clinical practice, objective, reliable diagnostic tools are needed. To address this, the study proposes a framework utilizing functional near-infrared spectroscopy (fNIRS) to distinguish healthy individuals, interictal migraine patients with and without aura. The approach focuses on prefrontal cortex (PFC) activity, extracting features from oxyhemoglobin, deoxyhemoglobin, and total hemoglobin in time, frequency, and time-frequency domains. XGBoost applied to time-frequency features of oxyhemoglobin in the left PFC demonstrated outstanding performance, achieving 92% balanced accuracy, 89% sensitivity, 95% specificity, and 89% F1 score. Non-invasive fNIRS with Machine Learning offers a promising, cost-effective alternative to traditional diagnostic methods, enhancing early and accurate diagnosis, leading to better-targeted treatments and improved outcomes. The study provides a strong foundation for future research and clinical applications in migraine diagnosis.