Olesen Alexander N, Jennum Poul, Peppard Paul, Mignot Emmanuel, Sorensen Helge B D
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1-4. doi: 10.1109/EMBC.2018.8513080.
We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polysomnogram recordings and subsequently tested on 230 independent recordings. Our best performing model yielded an accuracy of 84.1% and a Cohen's kappa of 0.746, improving on previous reported results by other groups also using only raw polysomnogram data. Most errors were made on non-REM stage 1 and 3 decisions, errors likely resulting from the definition of these stages. Further testing on independent cohorts is needed to verify performance for clinical use.
我们基于深度残差神经网络和原始多导睡眠图信号开发了一种自动睡眠阶段分类算法。简要来说，原始数据在随后被分类为五个睡眠阶段之一之前，要经过50个卷积层。在1850份多导睡眠图记录上训练了三种模型配置，随后在230份独立记录上进行测试。我们表现最佳的模型准确率为84.1%，科恩kappa系数为0.746，比其他同样仅使用原始多导睡眠图数据的研究小组之前报告的结果有所提高。大多数错误出现在非快速眼动睡眠1期和3期的判定上，这些错误可能是由这些阶段的定义导致的。需要在独立队列上进行进一步测试，以验证其临床应用性能。
