Migraine attack prediction using wearable biosensor data
Abstract
BackgroundMany migraine sufferers report difficulties in managing their attacks, leading to missed workdays, impaired cognitive function and reduced participation in social activities. This challenge underscores the importance of identifying reliable indicators of an impending attack. The present study aimed to develop and evaluate machine learning models for predicting migraine attacks based on continuous, non-invasive monitoring of autonomic nervous system activity.MethodsData from a wrist-worn biosensor device the Empatica EmbracePlus (Empatica Inc., Boston, MA, USA) capturing electrodermal activity, heart rate, skin temperature and metabolic equivalent of task were analysed using machine learning models to detect prodromal autonomic nervous system alterations and predict impending migraine headaches. Personalised models were developed using participant-specific model selection, whereas generalised transferability was assessed using a leave-one-participant-out approach.ResultsData from 27 participants were analysed, with an average monitoring period of 30.8 ± 12.9 days, during which 96 migraine attacks were recorded, corresponding to 3.6 ± 1.0 episodes per individual. Personalised prediction models, each reflecting a participant-specific best-selected configuration, achieved good overall predictive performance, with mean values of 0.82 ± 0.10 for accuracy, 0.75 ± 0.16 for precision, 0.72 ± 0.12 for recall and 0.73 ± 0.12 for the F1-score. The mean area under the receiver…
Read the paper
Related studies
- Calcium Ions in the Physiology and Pathology of the Central Nervous System
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare
- In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene
- In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy
- Targeting serotonin receptors with phytochemicals – an in-silico study
- In silico exploration of anti-prostate cancer compounds from differential expressed genes
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.