Phan Huy, Mertins Alfred, Baumert Mathias
IEEE Trans Biomed Eng. 2022 Dec;69(12):3612-3622. doi: 10.1109/TBME.2022.3174680. Epub 2022 Nov 21.
Despite the tremendous prog- ress recently made towards automatic sleep staging in adults, it is currently unknown if the most advanced algorithms generalize to the pediatric population, which displays distinctive characteristics in overnight polysomnography (PSG).
To answer the question, in this work, we conduct a large-scale comparative study on the state-of-the-art deep learning methods for pediatric automatic sleep staging. Six different deep neural networks with diverging features are adopted to evaluate a sample of more than 1,200 children across a wide spectrum of obstructive sleep apnea (OSA) severity.
Our experimental results show that the individual performance of automated pediatric sleep stagers when evaluated on new subjects is equivalent to the expert-level one reported on adults. Combining the six stagers into ensemble models further boosts the staging accuracy, reaching an overall accuracy of 88.8%, a Cohen's kappa of 0.852, and a macro F1-score of 85.8%. At the same time, the ensemble models lead to reduced predictive uncertainty. The results also show that the studied algorithms and their ensembles are robust to concept drift when the training and test data were recorded seven months apart and after clinical intervention.
However, we show that the improvements in the staging performance are not necessarily clinically significant although the ensemble models lead to more favorable clinical measures than the six standalone models.
Detailed analyses further demonstrate "almost perfect" agreement between the automatic stagers to one another and their similar patterns on the staging errors, suggesting little room for improvement.
尽管近期在成人自动睡眠分期方面取得了巨大进展，但目前尚不清楚最先进的算法是否适用于儿科人群，因为儿科人群在夜间多导睡眠图（PSG）中表现出独特的特征。
为回答这个问题，在本研究中，我们对用于儿科自动睡眠分期的最先进深度学习方法进行了大规模比较研究。采用六种具有不同特征的深度神经网络，对超过1200名患有不同程度阻塞性睡眠呼吸暂停（OSA）的儿童样本进行评估。
我们的实验结果表明，自动儿科睡眠分期器在新受试者上进行评估时的个体性能与报道的成人专家水平相当。将六种分期器组合成集成模型进一步提高了分期准确性，总体准确率达到88.8%，科恩kappa系数为0.852，宏F1分数为85.8%。同时，集成模型降低了预测不确定性。结果还表明，当训练和测试数据相隔七个月且经过临床干预时，所研究的算法及其集成对概念漂移具有鲁棒性。
然而，我们表明，尽管集成模型比六个独立模型产生了更有利的临床指标，但分期性能的改善不一定具有临床意义。
详细分析进一步证明了自动分期器之间“几乎完美”的一致性以及它们在分期错误上的相似模式，表明改进空间很小。
