Advanced Brain Monitoring Inc., Carlsbad, CA, USA; Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA.
J Sleep Res. 2014 Apr;23(2):211-21. doi: 10.1111/jsr.12105. Epub 2013 Dec 7.
Accuracy and limitations of automatic scoring of sleep stages and electroencephalogram arousals from a single derivation (Fp1 -Fp2 ) were studied in 29 healthy adults using a portable wireless polysomnographic recorder. All recordings were scored five times: twice by a referent scorer who viewed the standard polysomnographic montage and observed the American Academy of Sleep Medicine rules (referent scoring and blind rescoring); and once by the same scorer who viewed only the Fp1 -Fp2 signal (alternative scoring), by another expert from the same institution, and by the algorithm. Automatic, alternative and independent expert scoring were compared with the referent scoring on an epoch-by-epoch basis. The algorithm's agreement with the reference (81.0%, Cohen's κ = 0.75) was comparable to the inter-rater agreement (83.3%, Cohen's κ = 0.78) or agreement between the referent scoring and manual scoring of the frontopolar derivation (80.7%, Cohen's κ = 0.75). Most misclassifications by the algorithm occurred during uneventful wake/sleep transitions, whereas cortical arousals, rapid eye movement and stable non-rapid eye movement sleep were detected accurately. The algorithm yielded accurate estimates of total sleep time, sleep efficiency, sleep latency, arousal indices and times spent in different stages. The findings affirm the utility of automatic scoring of stages and arousals from a single frontopolar derivation as a method for assessment of sleep architecture in healthy adults.
准确性和局限性的自动评分的睡眠阶段和脑电图唤醒从一个单一的派生 (Fp1 - Fp2) 研究了 29 例健康成年人使用便携式无线多导睡眠记录。所有记录都被评为五次:两次由参考评分谁查看标准多导睡眠图和观察美国睡眠医学协会规则 (参考评分和盲评分);一次由相同的评分只查看 Fp1 - Fp2 信号 (替代评分),由同一机构的另一位专家,和算法。自动、替代和独立专家评分与参考评分进行逐时比较。算法与参考的一致性 (81.0%, Cohen κ = 0.75) 与评分者间的一致性 (83.3%, Cohen κ = 0.78) 或参考评分和手动评分的额叶导联 (80.7%, Cohen κ = 0.75)。算法的最错误分类发生在无事件的清醒/睡眠转换期间,而皮质唤醒、快速眼动和稳定的非快速眼动睡眠被准确地检测到。算法产生了总睡眠时间、睡眠效率、睡眠潜伏期、觉醒指数和不同阶段的时间的准确估计。研究结果证实了自动评分的阶段和从一个单一的额叶导联唤醒作为一种评估健康成年人睡眠结构的方法的实用性。