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在患有的儿童中,在减少道上的自动睡眠分期

Renee Proost¹, Elisabeth Heremans², Lieven Lagae¹

¹Pediatric Neurology Department, University Hospitals Leuven, KU Leuven, Leuven, Belgium.

Frontiers in neurology

|May 27, 2024

概括

这项研究验证了儿童的自动睡眠分期算法. 该算法在没有和有良好的控制的儿童中显示出高准确性,在耐药患者中显示出可接受的性能.

科学领域:

儿科神经学儿科神经学
睡眠医学睡眠医学
生物医学工程生物医学工程

背景情况:

准确的睡眠分期对于诊断儿童睡眠障碍和神经疾病至关重要.
传统的手动视频脑电图 (EEG) 睡眠评分是耗时且主观的.
自动睡眠分阶段算法为高效和客观的睡眠分析提供了潜在的解决方案.

研究的目的:

为了验证一个自动睡眠分期算法 (SeqSleepNet) 使用医院视频EEG在儿科患者群体.
为了比较算法的表现在没有的儿童,有良好的控制 (WCE) 和耐药 (DRE) 中.

主要方法:

在176名儿童 (4-18岁) 中记录了夜间视频EEG,电眼图 (EOG) 和下巴电肌图 (EMG).
手动睡眠阶段化作为地面真理.
一个端到端的层次循环神经网络 (SeqSleepNet) 使用C4-A1EEG,EOG和EMG通道进行了自动睡眠分阶段.

主要成果:

自动化算法实现了5类睡眠分期准确率84.7% (没有),83.5% (WCE) 和80.8% (DRE).
各组的卡帕值分别为0.79,0.77和0.73.
F1评分显示了Wake (0.91),N2 (0.83),N3 (0.84) 和REM (0.86) 睡眠的高性能,N1 (0.50) 的精度较低.

结论:

SeqSleepNet算法在没有的儿童和患有WCE的儿童中显示出睡眠分期的高准确性.
患有DRE的儿童的表现是可以接受的,但较低,可能是由于N1睡眠流行率和型出院等因素.
该算法可靠地检测REM睡眠,即使在儿童中,DRE也受到显著影响.

关键词:

快速眼睛运动 (REM) 睡眠是什么?自动化睡眠分期睡眠分期孩子们的孩子们的孩子们的孩子们.是一种.机器学习是机器学习.

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