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.

监测自闭症谱系障碍 (ASD) 儿童的睡眠模式并了解睡眠质量如何影响他们的日间行为是一个受到非常有限关注的重要问题。多导睡眠图 (PSG) 通常用作评估儿童和成人睡眠质量的金标准。然而，用作 PSG 一部分的传感器的侵入性本身会影响睡眠，因此不适合 ASD 儿童。在这项研究中，我们使用心冲击图 (BCG) 信号评估了一种用于家庭长期睡眠质量监测的不显眼且价格低廉的床系统。使用来自该智能床系统的 BCG 信号，我们将“烦躁”定义为替代睡眠质量估计量。使用此睡眠功能，我们基于前 1-8 夜的睡眠构建了白天行为的预测模型。具体来说，我们使用两种监督机器学习算法，即支持向量机 (SVM) 和人工神经网络 (ANN)。对于所有日间行为，我们分别使用 SVM 和 ANN 分类器正确预测行为问题的准确度超过 78% 和 79%。我们的发现表明了我们设计的床系统的有用性以及不安特征如何提高预测性能。