论文标题
使用脉搏血氧仪信号通过复发性神经网络分类睡眠效果阶段
Classifying sleep-wake stages through recurrent neural networks using pulse oximetry signals
论文作者
论文摘要
自主神经系统的调节随着睡眠阶段的变化而变化，导致生理变量的变化。我们利用这些变化，目的是使用脉搏血氧仪信号在清醒或入睡中对睡眠阶段进行分类。我们将复发性神经网络应用于心率和周围氧饱和信号，每30秒对睡眠阶段进行分类。该网络架构由双向封闭式复发单元（GRUS）和软磁层层组成，以对输出进行分类。在本文中，我们使用了来自Sleep Heart Health研究数据集的5000名患者。 2500名患者被用于训练网络，并使用两个1250个子集来验证和测试训练有素的模型。在测试阶段，获得的最佳结果为90.13％的精度，94.13％的灵敏度，80.26％的特异性，92.05％的精度和84.68％的负预测值。此外，Cohen的Kappa系数为0.74，实际睡眠时间的平均绝对错误百分比为8.9％。当提出的网络的性能与最先进的算法使用更具信息性信号（除EEG之外）时，它们的性能与最新的算法相当。
The regulation of the autonomic nervous system changes with the sleep stages causing variations in the physiological variables. We exploit these changes with the aim of classifying the sleep stages in awake or asleep using pulse oximeter signals. We applied a recurrent neural network to heart rate and peripheral oxygen saturation signals to classify the sleep stage every 30 seconds. The network architecture consists of two stacked layers of bidirectional gated recurrent units (GRUs) and a softmax layer to classify the output. In this paper, we used 5000 patients from the Sleep Heart Health Study dataset. 2500 patients were used to train the network, and two subsets of 1250 were used to validate and test the trained models. In the test stage, the best result obtained was 90.13% accuracy, 94.13% sensitivity, 80.26% specificity, 92.05% precision, and 84.68% negative predictive value. Further, the Cohen's Kappa coefficient was 0.74 and the average absolute error percentage to the actual sleep time was 8.9%. The performance of the proposed network is comparable with the state-of-the-art algorithms when they use much more informative signals (except those with EEG).
