Department of Biological Sciences, Wright State University, Dayton, OH 45435, USA.
Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435, USA.
Sensors (Basel). 2019 Mar 22;19(6):1417. doi: 10.3390/s19061417.
We use self-report and electrodermal activity (EDA) wearable sensor data from 77 nights of sleep of six participants to test the efficacy of EDA data for sleep monitoring. We used factor analysis to find latent factors in the EDA data, and used causal model search to find the most probable graphical model accounting for self-reported sleep efficiency (SE), sleep quality (SQ), and the latent factors in the EDA data. Structural equation modeling was used to confirm fit of the extracted graph to the data. Based on the generated graph, logistic regression and naïve Bayes models were used to test the efficacy of the EDA data in predicting SE and SQ. Six EDA features extracted from the total signal over a night's sleep could be explained by two latent factors, EDA Magnitude and EDA Storms. EDA Magnitude performed as a strong predictor for SE to aid detection of substantial changes in time asleep. The performance of EDA Magnitude and SE in classifying SQ demonstrates promise for using a wearable sensor for sleep monitoring. However, our data suggest that obtaining a more accurate sensor-based measure of SE will be necessary before smaller changes in SQ can be detected from EDA sensor data alone.
我们使用来自六名参与者 77 晚睡眠的自我报告和皮肤电活动 (EDA) 可穿戴传感器数据来测试 EDA 数据在睡眠监测中的功效。我们使用因子分析在 EDA 数据中找到潜在因素，并使用因果模型搜索找到最有可能解释自我报告的睡眠效率 (SE)、睡眠质量 (SQ) 和 EDA 数据中潜在因素的图形模型。结构方程建模用于确认从数据中提取的图形的拟合度。基于生成的图形，逻辑回归和朴素贝叶斯模型用于测试 EDA 数据在预测 SE 和 SQ 中的功效。从一夜睡眠的总信号中提取的六个 EDA 特征可以用两个潜在因素来解释，即 EDA 幅度和 EDA 风暴。EDA 幅度作为 SE 的强预测因子，有助于检测睡眠时间的实质性变化。EDA 幅度和 SE 在 SQ 分类中的性能表明，使用可穿戴传感器进行睡眠监测具有潜力。然而，我们的数据表明，在仅从 EDA 传感器数据中检测到 SQ 的较小变化之前，获得更准确的基于传感器的 SE 测量值将是必要的。
