Heart rate variability as a predictor of migraine: Sleep-time data analysis of pre-migraine nights

Abstract

Background

Migraine is a complex neurological disorder characterized by recurrent, often debilitating headaches. Current evidence suggests that autonomic nervous system (ANS) alterations play a significant role in migraine pathophysiology, affecting sensory, limbic, and homeostatic processing. Heart rate variability (HRV), a well-established, noninvasive marker of ANS function, is associated with migraine severity and treatment efficacy.

Objective

In this study, we aimed to evaluate the use of wearable sensor technology in predicting migraine attacks by monitoring changes in the ANS during the prodrome phase.

Methods

We recruited 23 migraine sufferers and analyzed HRV during nocturnal sleep using wearable biosensors and machine learning, extracting HRV features from BVP signals and applying feature engineering to predict migraine episodes.

Results

The analysis of HRV provides an important approach to migraine attack prediction, revealing significant individual variability in physiological responses. Overall, these results lay the groundwork for developing more effective and personalized migraine prediction models, which could lead to earlier interventions and improved participant outcomes. Future research should consider controlled inclusion of post-migraine nights, potentially leveraging additional statistical or machine learning techniques to mitigate misclassification risks while capturing these transitional dynamics.