基于脑电图的睡眠指数和监督式机器学习作为儿童自动睡眠分类的合适工具。

An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children.

作者信息

van Twist Eris, Hiemstra Floor W, Cramer Arnout B G, Verbruggen Sascha C A T, Tax David M J, Joosten Koen, Louter Maartje, Straver Dirk C G, de Hoog Matthijs, Kuiper Jan Willem, de Jonge Rogier C J

机构信息

Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands.

Department of Intensive Care, Leiden University Medical Centre, Leiden, The Netherlands.

出版信息

J Clin Sleep Med. 2024 Mar 1;20(3):389-397. doi: 10.5664/jcsm.10880.

DOI:10.5664/jcsm.10880

PMID:37869968

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11019221/

Abstract

STUDY OBJECTIVES

Although sleep is frequently disrupted in the pediatric intensive care unit, it is currently not possible to perform real-time sleep monitoring at the bedside. In this study, spectral band powers of electroencephalography data are used to derive a simple index for sleep classification.

METHODS

Retrospective study at Erasmus MC Sophia Children's Hospital, using hospital-based polysomnography recordings obtained in non-critically ill children between 2017 and 2021. Six age categories were defined: 6-12 months, 1-3 years, 3-5 years, 5-9 years, 9-13 years, and 13-18 years. Candidate index measures were derived by calculating spectral band powers in different frequent frequency bands of smoothed electroencephalography. With the best performing index, sleep classification models were developed for two, three, and four states via decision tree and five-fold nested cross-validation. Model performance was assessed across age categories and electroencephalography channels.

RESULTS

In total 90 patients with polysomnography were included, with a mean (standard deviation) recording length of 10.3 (1.1) hours. The best performance was obtained with the gamma to delta spectral power ratio of the F4-A1 and F3-A1 channels with smoothing. Balanced accuracy was 0.88, 0.74, and 0.57 for two-, three-, and four-state classification. Across age categories, balanced accuracy ranged between 0.83 and 0.92 and 0.72 and 0.77 for two- and three-state classification, respectively.

CONCLUSIONS

We propose an interpretable and generalizable sleep index derived from single-channel electroencephalography for automated sleep monitoring at the bedside in non-critically ill children ages 6 months to 18 years, with good performance for two- and three-state classification.

CITATION

van Twist E, Hiemstra FW, Cramer ABG, et al. An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children. . 2024;20(3):389-397.

摘要

研究目的

尽管儿科重症监护病房中睡眠经常被打断，但目前无法在床边进行实时睡眠监测。在本研究中，脑电图数据的频谱带功率被用于得出一个用于睡眠分类的简单指标。

方法

在伊拉斯姆斯医学中心索菲亚儿童医院进行回顾性研究，使用2017年至2021年间在非危重症儿童中获得的基于医院的多导睡眠图记录。定义了六个年龄类别：6至12个月、1至3岁、3至5岁、5至9岁、9至13岁和13至18岁。候选指标通过计算平滑脑电图不同频率带的频谱带功率得出。使用表现最佳的指标，通过决策树和五重嵌套交叉验证为二、三、四种状态开发睡眠分类模型。在不同年龄类别和脑电图通道上评估模型性能。

结果

总共纳入了90例有多导睡眠图的患者，平均（标准差）记录时长为10.3（1.1）小时。F4 - A1和F3 - A1通道经平滑处理后的伽马与德尔塔频谱功率比表现最佳。二、三、四种状态分类的平衡准确率分别为0.88、0.74和0.57。在不同年龄类别中，二、三状态分类的平衡准确率分别在0.83至0.92和0.72至0.77之间。