Garcia-Molina Gary, Abtahi Farhad, Lagares-Lemos Miguel
Philips Research North America, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2255-8. doi: 10.1109/EMBC.2012.6346411.
Automatic sleep staging from convenient and unobtrusive sensors has received considerable attention lately because this can enable a large range of potential applications in the clinical and consumer fields. In this paper the focus is on achieving non-REM (NREM) sleep staging from ocular electrodes. From these signals, specific patterns related to sleep such as slow eye movements, K-complexes, eye blinks, and spectral features are estimated. Although such patterns are characteristic of the Electroencephalogram, they can also be visible to a lesser extent on signals from ocular electrodes. Automatic sleep staging was implemented using two approaches: i) based on a state-machine and ii) using a neural network. The first one relied on the recommendations of the American Academy of Sleep Medicine, and the second one used a multilayer perceptron which was trained on manually sleep-staged data. Results were obtained on the data of five volunteers who participated in a nap experiment. Manual sleep staging of this data, performed by an expert, was used as reference. Five stages were considered, namely wake with eyes open, wake with eyes closed, and sleep stages N1, N2, and N3. The results were characterized in terms of confusion matrices from which the Cohen's κ coefficients were estimated. The values of κ for both the state-machine and neural-network based automatic sleep staging approaches were 0.79 and 0.59 respectively. Thus, the state-machine based approach shows a very good agreement with manual staging of sleep-data.
利用便捷且不引人注意的传感器进行自动睡眠分期最近受到了广泛关注，因为这能够在临床和消费领域实现大量潜在应用。本文的重点是通过眼电极实现非快速眼动（NREM）睡眠分期。从这些信号中，可以估计出与睡眠相关的特定模式，如慢眼动、K复合波、眨眼和频谱特征。尽管这些模式是脑电图的特征，但在眼电极信号上也能在较小程度上看到。自动睡眠分期采用了两种方法：i）基于状态机；ii）使用神经网络。第一种方法依赖于美国睡眠医学学会的建议，第二种方法使用了在手动睡眠分期数据上训练的多层感知器。对参与午睡实验的五名志愿者的数据进行了分析。由专家对这些数据进行的手动睡眠分期被用作参考。考虑了五个阶段，即睁眼清醒、闭眼清醒以及睡眠阶段N1、N2和N3。结果通过混淆矩阵进行表征，并据此估计科恩κ系数。基于状态机和神经网络的自动睡眠分期方法的κ值分别为0.79和0.59。因此，基于状态机的方法与睡眠数据的手动分期显示出非常好的一致性。
