Time-Series Prediction of Proximal Aggression Onset in Minimally-Verbal Youth with Autism Spectrum Disorder Using Physiological Biosignals
This addresses a critical safety issue for caregivers and healthcare providers of minimally-verbal youth with autism, though it is incremental as it builds on prior hypotheses about physiological changes.
The study tackled the problem of predicting imminent aggressive behavior in minimally-verbal youth with autism spectrum disorder by analyzing physiological biosignals, achieving proof-of-concept results with models that could predict aggression onset one minute before it occurs.
It has been suggested that changes in physiological arousal precede potentially dangerous aggressive behavior in youth with autism spectrum disorder (ASD) who are minimally verbal (MV-ASD). The current work tests this hypothesis through time-series analyses on biosignals acquired prior to proximal aggression onset. We implement ridge-regularized logistic regression models on physiological biosensor data wirelessly recorded from 15 MV-ASD youth over 64 independent naturalistic observations in a hospital inpatient unit. Our results demonstrate proof-of-concept, feasibility, and incipient validity predicting aggression onset 1 minute before it occurs using global, person-dependent, and hybrid classifier models.