Tsinalis Orestis, Matthews Paul M, Guo Yike
Department of Computing, Imperial College London, London, UK.
Division of Brain Sciences, Imperial College London, London, UK.
Ann Biomed Eng. 2016 May;44(5):1587-97. doi: 10.1007/s10439-015-1444-y. Epub 2015 Oct 13.
We developed a machine learning methodology for automatic sleep stage scoring. Our time-frequency analysis-based feature extraction is fine-tuned to capture sleep stage-specific signal features as described in the American Academy of Sleep Medicine manual that the human experts follow. We used ensemble learning with an ensemble of stacked sparse autoencoders for classifying the sleep stages. We used class-balanced random sampling across sleep stages for each model in the ensemble to avoid skewed performance in favor of the most represented sleep stages, and addressed the problem of misclassification errors due to class imbalance while significantly improving worst-stage classification. We used an openly available dataset from 20 healthy young adults for evaluation. We used a single channel of EEG from this dataset, which makes our method a suitable candidate for longitudinal monitoring using wearable EEG in real-world settings. Our method has both high overall accuracy (78%, range 75-80%), and high mean [Formula: see text]-score (84%, range 82-86%) and mean accuracy across individual sleep stages (86%, range 84-88%) over all subjects. The performance of our method appears to be uncorrelated with the sleep efficiency and percentage of transitional epochs in each recording.
我们开发了一种用于自动睡眠阶段评分的机器学习方法。我们基于时频分析的特征提取经过了微调，以捕捉美国睡眠医学学会手册中所述的特定睡眠阶段信号特征，人类专家遵循该手册。我们使用堆叠稀疏自编码器集成的集成学习来对睡眠阶段进行分类。我们在集成中的每个模型中对各个睡眠阶段使用类平衡随机采样，以避免偏向最具代表性的睡眠阶段的性能偏差，并解决由于类不平衡导致的错误分类问题，同时显著提高最差阶段的分类效果。我们使用来自20名健康年轻成年人的公开可用数据集进行评估。我们使用该数据集中的单通道脑电图，这使得我们的方法成为在现实环境中使用可穿戴脑电图进行纵向监测的合适候选方法。我们的方法在所有受试者中具有较高的总体准确率（78%，范围75 - 80%）、较高的平均F1分数（84%，范围82 - 86%）以及各个睡眠阶段的平均准确率（86%，范围84 - 88%）。我们方法的性能似乎与每次记录中的睡眠效率和过渡期百分比无关。
