OBJECTIVE Significant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals. METHODS A deep neural network was trained on 2,889 PSGs to detect cortical arousals and wakefulness in 1-second intervals. Furthermore, the relationship between MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was analyzed in 1447 MSLT instances in 873 subjects. RESULTS In a dataset of 1,026 PSGs, the MAD achieved an F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by nine expert technicians, the MAD performed comparable to four and significantly outperformed five expert technicians for arousal detection. After controlling for known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds (p = 0.0075). CONCLUSIONS The MAD performed better or comparable to human expert scorers. The MAD-predicted arousals were shown to be significant predictors of MSL. SIGNIFICANCE This study validates a fully automatic method for scoring arousals in PSGs.

中文翻译：

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自动检测睡眠中的皮质唤醒及其对白天嗜睡的影响

目的 在多导睡眠图记录 (PSG) 的唤醒手动评分中发现显着的评分者间变异。我们提出了一种全自动方法，即多模态唤醒检测器（MAD），用于检测唤醒。方法 在 2,889 个 PSG 上训练深度神经网络，以检测 1 秒间隔内的皮质唤醒和觉醒。此外，在 873 名受试者的 1447 个 MSLT 实例中，分析了 PSG 上的 MAD 预测标签与多次睡眠潜伏期测试 (MSLT) 上的第二天平均睡眠潜伏期 (MSL)（反映白天嗜睡程度）之间的关系。结果 在 1,026 个 PSG 的数据集中，MAD 的唤醒检测 F1 得分为 0.76，而觉醒预测的准确度为 0.95。在由 9 名专家技术人员评分的 60 个 PSG 中，MAD 在唤醒检测方面的表现与 4 名专家技术人员相当，并且显着优于 5 名专家技术人员。控制已知协变量后，觉醒指数加倍与 MSL 平均下降 40 秒相关（p = 0.0075）。结论 MAD 的表现更好或与人类专家评分者相当。MAD 预测的唤醒被证明是 MSL 的重要预测因子。意义 本研究验证了 PSG 唤醒评分的全自动方法。