Cortical arousals are transient events of disturbed sleep that occur spontaneously or in response to stimuli such as apneic events. The gold standard for arousal detection in human polysomnographic recordings (PSGs) is manual annotation by expert human scorers, a method with significant interscorer variability. In this study, we developed an automated method, the Multimodal Arousal Detector (MAD), to detect arousals using deep learning methods. The MAD was trained on 2,889 PSGs to detect both 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. In a dataset of 1,026 PSGs, the MAD achieved a F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by multiple human expert technicians, the MAD significantly outperformed the average human scorer for arousal detection with a difference in F1 score of 0.09. After controlling for other known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds ($β$ = -0.67, p = 0.0075). The MAD outperformed the average human expert and the MAD-predicted arousals were shown to be significant predictors of MSL, which demonstrate clinical validity the MAD.

翻译：皮层觉醒是睡眠中发生的短暂性干扰事件，可自发出现或由诸如呼吸暂停事件等刺激诱发。人类多导睡眠图记录中觉醒检测的金标准是专家评分员的人工标注，但该方法存在显著的评分者间差异。本研究开发了一种自动化方法——多模态觉醒检测器，利用深度学习技术检测觉醒。该检测器基于2,889份多导睡眠图数据进行训练，以1秒为间隔检测皮层觉醒与清醒状态。此外，在873名受试者的1,447次多重睡眠潜伏期测试实例中，分析了多导睡眠图上MAD预测标签与次日平均睡眠潜伏期（反映日间嗜睡的指标）的关联性。在1,026份多导睡眠图数据集中，MAD对觉醒检测的F1分数达0.76，清醒状态预测准确率为0.95。在由多位人类专家技师评分的60份多导睡眠图中，MAD在觉醒检测方面显著优于人类评分员平均水平，F1分数差异达0.09。在控制其他已知协变量后，觉醒指数翻倍与平均睡眠潜伏期减少40秒相关（β = -0.67, p = 0.0075）。MAD的表现优于人类专家平均水平，且其预测的觉醒被证明是平均睡眠潜伏期的显著预测因子，这验证了MAD的临床有效性。