Sadeghi Reza, Banerjee Tanvi, Hughes John
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5571-5574. doi: 10.1109/EMBC44109.2020.9175629.
Sleep quality (SQ) is one of the most well-known factors in daily work performance. Sleep is usually analyzed using polysomnography (PSG) by attaching electrodes to the bodies of participants, which is likely sleep destructive. As a result, investigating SQ using a more easy-to-use and cost-effective methodology is currently a hot topic. To avoid overfitting concerns, one likely methodology for predicting SQ can be achieved by reducing the number of utilized signals. In this paper, we propose three methodologies based on electronic health records and heart rate variability (HRV). To evaluate the performance of the proposed methods, several experiments have been conducted using the Osteoporotic Fractures in Men (MrOS) sleep dataset. The experimental results reveal that a deep neural network methodology can achieve an accuracy of 0.6 in predicting light, medium, and deep SQ using only ECG signals recorded during PSG. This outcome demonstrates the capability of using HRV features, which are effortlessly measurable by easy-to-use and cost-effective wearable devices, in predicting SQ.
睡眠质量（SQ）是日常工作表现中最为人熟知的因素之一。睡眠通常通过多导睡眠图（PSG）进行分析，即给参与者身体附着电极，但这可能会破坏睡眠。因此，使用更易于使用且成本效益更高的方法来研究睡眠质量目前是一个热门话题。为避免过度拟合问题，一种可能的预测睡眠质量的方法是通过减少所使用信号的数量来实现。在本文中，我们提出了三种基于电子健康记录和心率变异性（HRV）的方法。为评估所提方法的性能，我们使用男性骨质疏松性骨折（MrOS）睡眠数据集进行了多项实验。实验结果表明，一种深度神经网络方法仅使用在PSG期间记录的心电图信号就能在预测轻度、中度和深度睡眠质量方面达到0.6的准确率。这一结果证明了利用HRV特征（可通过易于使用且成本效益高的可穿戴设备轻松测量）来预测睡眠质量的能力。
