Smart bed based daytime behavior prediction in Children with autism spectrum disorder - A Pilot Study

Abstract

Monitoring the sleep patterns of children with autism spectrum disorders (ASD) and understanding how sleep quality influences their daytime behavior is an important issue that has received very limited attention. Polysomnography (PSG) is commonly used as a gold standard for evaluating sleep quality in children and adults. However, the intrusive nature of sensors used as part of PSG can themselves affect sleep and is, therefore, not suitable for children with ASD. In this study, we evaluate an unobtrusive and inexpensive bed system for in-home, long-term sleep quality monitoring using ballistocardiogram (BCG) signals. Using the BCG signals from this smart bed system, we define "restlessness" as a surrogate sleep quality estimator. Using this sleep feature, we build predictive models for daytime behavior based on 1-8 previous nights of sleep. Specifically, we use two supervised machine learning algorithms namely support vector machine (SVM) and artificial neural network (ANN). For all daytime behaviors, we achieve more than 78% and 79% accuracy of correctly predicting behavioral issues with both SVM and ANN classifiers, respectively. Our findings indicate the usefulness of our designed bed system and how the restlessness feature can improve the prediction performance.