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监督机器学习对心电图功能进行了监督,以对非临界疾病儿童的睡眠进行分类
Eris van Twist1, Anne M Meester2, Arnout B G Cramer1
1Department of Neonatal and Pediatric Intensive Care, Division of Pediatric Intensive Care, Erasmus MC Sophia Children's Hospital, Rotterdam, The Netherlands.
概括
使用心电图 (ECG) 数据的机器学习模型现在可以非侵入性地对儿童的睡眠进行分类. 这提供了一个有前途的床边工具,用于监测非危急病儿科患者的睡眠模式.
科学领域:
- 生物医学工程 生物医学工程
- 计算神经科学是一种神经科学.
- 儿科睡眠医学 儿科睡眠医学
背景情况:
- 儿科重症监护室中无法实时监测床边睡眠,尽管经常有睡眠障碍.
- 心血管动态在睡眠期间受到自主神经系统的影响,这表明心电图是潜在的数据来源.
研究的目的:
- 开发和验证使用儿童心电图 (ECG) 数据进行自动化睡眠分类的监督机器学习模型.
- 评估这些模型在不同年龄组和睡眠状态分类中的表现.
主要方法:
- 对90名非危急病症儿童 (6个月至18岁) 的多睡眠记录进行了回顾性分析.
- 在时间,频率和非线性领域提取ECG特征.
- 使用后勤回归,随机森林和XGBoost开发2到5状态睡眠分类模型,并进行5倍嵌套交叉验证.
主要成果:
- 模型实现了接收机操作员特征曲线下的面积为0.72-0.78.
- 均衡准确度从0.70-0.72为2状态分类到0.41-0.42为5状态分类.
- XGBoost通常表现最好,除了5个状态分类,后勤回归优异.
结论:
- 基于心电图的机器学习模型提供了一种有前途的,非侵入性的方法,用于对非危急疾病儿童的床边睡眠分类.
- 这些模型在这个儿科患者群体中,在2状态和3状态睡眠分类方面表现中等至良好.
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