论文标题
大规模多渠道小儿EEG的自动睡眠评分
Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEG
论文作者
论文摘要
睡眠对婴儿，儿童和青少年的健康尤为重要，睡眠评分是准确诊断和治疗潜在威胁生命的状况的第一步。但是，与成人睡眠相比，在机器学习的情况下，小儿睡眠的研究严重不足，而成人的睡眠评分算法通常在婴儿身上表现不佳。在这里，我们介绍了最近在标准临床护理期间收集的最近大规模的小儿睡眠研究数据集中的第一个自动睡眠评分结果。我们开发了一个基于变压器的模型，该模型学会从数百万多通道脑电图（EEG）睡眠时期分类为78％的总体准确性。此外，我们根据患者人口统计学和脑电图通道对模型性能进行了深入的分析。结果表明，对小儿睡眠的机器学习研究的需求日益增长。
Sleep is particularly important to the health of infants, children, and adolescents, and sleep scoring is the first step to accurate diagnosis and treatment of potentially life-threatening conditions. But pediatric sleep is severely under-researched compared to adult sleep in the context of machine learning for health, and sleep scoring algorithms developed for adults usually perform poorly on infants. Here, we present the first automated sleep scoring results on a recent large-scale pediatric sleep study dataset that was collected during standard clinical care. We develop a transformer-based model that learns to classify five sleep stages from millions of multi-channel electroencephalogram (EEG) sleep epochs with 78% overall accuracy. Further, we conduct an in-depth analysis of the model performance based on patient demographics and EEG channels. The results point to the growing need for machine learning research on pediatric sleep.
