Predicting seizure episodes and high-risk events in autism through adverse behavioral patterns

Objective.To determine whether historical behavior data can predict the occurrence of high-risk behavioral or Seizure events in individuals with profound Autism Spectrum Disorder (ASD), thereby facilitating early intervention and improved support.Approach.We conducted an analysis of nine years of behavior and seizure data from 353 individuals with ASD. Our analysis focused on the seven most common behaviors labeled by a human, while all other behaviors were grouped into an 'other' category, resulting in a total of eight behavior categories. Using a deep learning algorithm, we predicted the occurrence of seizures and high-risk behavioral events for the following day based on data collected over the most recent 14 d period. We employed permutation-based statistical tests to assess the significance of our predictive performance.Main results.Our model achieved accuracies of 70.5% for seizures, 78.3% for aggression, 80.2% for SIB, and 85.7% for elopement. All results were significant for more than 85% of the population. These findings suggest that high-risk behaviors can serve as early indicators not only of subsequent challenging behaviors but also of upcoming seizure events.Significance.By demonstrating, for the first time, that behavioral patterns can predict seizures as well as adverse behaviors, this approach expands the clinical utility of predictive modeling in ASD. Early warning systems derived from these predictions can guide timely interventions, enhance inclusion in educational and community settings, and improve quality of life by helping anticipate and mitigate severe behavioral and medical events.