Long-Term EEG-Based Modeling and Classification of Migraine Phases Using Hidden Markov Models

Safoura  Ashoorisefat; Mohammad  Pooyan; Alia  Saberi

doi:10.18502/fbt.v13i2.21949

Safoura Ashoorisefat Department of Biomedical Engineering, Qa. C., Islamic Azad University, Qazvin, Iran
Mohammad Pooyan Biomedical Engineering Department, Engineering Faculty, Shahed University, Tehran, Iran
Alia Saberi Department of Biomedical Engineering, Qa. C., Islamic Azad University, Qazvin, Iran

DOI: https://doi.org/10.18502/fbt.v13i2.21949

Keywords: Electroencephalogram Signal; Migraine; Hidden Markov Model; Dynamic Mode Decomposition; Wavelet Transform; Phase Classification; Prediction.

Abstract

Purpose: Migraine is a complex neurological disorder characterized by dynamic alterations in brain activity during multiple phases: interictal (baseline), preictal, ictal, and postictal. This study aims to model and differentiate these migraine phases using Electroencephalogram (EEG) and a Hidden Markov Model (HMM).

Materials and Methods: EEG signals were collected from each subject over several months through frequent, short sessions often multiple times per day. The recordings were temporally aligned with self-reported symptom diaries, allowing for precise labeling of migraine phases. A comprehensive set of features was extracted from the EEG signals, including spectral, temporal, and nonlinear measures such as Dynamic Mode Decomposition (DMD) and Katz Fractal Dimension (KFD) across various frequency bands. Despite the limited number of participants, the dense long-term recordings captured multiple migraine episodes, enabling reliable phase modeling.

Results: The HMM identified migraine-specific neural patterns, achieving an average classification accuracy of approximately 87% for all 15 patients, with individual patient performance ranging from 70% to 95%, depending on signal length and normalization. Only three patients are shown in detail in the results section as illustrative examples.

Conclusion: The HMM identified distinguishable neural patterns corresponding to migraine states, suggesting the feasibility of temporal EEG modeling for clinical applications in personalized migraine management.