BACKGROUND & AIMS:
STUDY OBJECTIVE:To assess the performance of automatic sleep scoring software (ASEEGA) based on a single EEG channel comparatively with manual scoring (2 experts) of conventional full polysomnograms.
DESIGN:Polysomnograms from 15 healthy individuals were scored by 2 independent experts using conventional R&K rules. The results were compared to those of ASEEGA scoring on an epoch-by-epoch basis.
SETTING:Sleep laboratory in the physiology department of a teaching hospital.
PARTICIPANTS:Fifteen healthy volunteers.
MEASUREMENTS AND RESULTS:The epoch-by-epoch comparison was based on classifying into 2 states (wake/sleep), 3 states (wake/REM/ NREM), 4 states (wake/REM/stages 1-2/SWS), or 5 states (wake/REM/ stage 1/stage 2/SWS). The obtained overall agreements, as quantified by the kappa coefficient, were 0.82, 0.81, 0.75, and 0.72, respectively. Furthermore, obtained agreements between ASEEGA and the expert consensual scoring were 96.0%, 92.1%, 84.9%, and 82.9%, respectively. Finally, when classifying into 5 states, the sensitivity and positive predictive value of ASEEGA regarding wakefulness were 82.5% and 89.7%, respectively. Similarly, sensitivity and positive predictive value regarding REM state were 83.0% and 89.1%.
CONCLUSIONS:Our results establish the face validity and convergent validity of ASEEGA for single-channel sleep analysis in healthy individuals. ASEEGA appears as a good candidate for diagnostic aid and automatic ambulant scoring.
背景与目标:
目的:评估基于单个EEG通道的自动睡眠评分软件(ASEEGA)的性能,并与传统的完整多导睡眠图的手动评分(2位专家)进行比较。
设计:由15位健康个体的多导睡眠图由2位独立专家使用常规R&K规则评分。将结果与ASEEGA得分进行逐时比较。
地点:教学医院生理科的睡眠实验室。
参加者:十五名健康志愿者。
测量和结果:每个时期的比较基于分类为2个状态(唤醒/睡眠),3个状态(唤醒/ REM / NREM),4个状态(唤醒/ REM / 1-2级/ SWS)或5种状态(唤醒/ REM /阶段1 /阶段2 / SWS)。通过卡伯系数量化,所获得的总体一致性分别为0.82、0.81、0.75和0.72。此外,ASEEGA与专家共识得分之间达成的协议分别为96.0%,92.1%,84.9%和82.9%。最后,当分为五个状态时,ASEEGA的清醒敏感性和阳性预测值分别为82.5%和89.7%。同样,关于REM状态的敏感性和阳性预测值分别为83.0%和89.1%。
结论:我们的结果建立了ASEEGA在健康个体单通道睡眠分析中的脸部有效性和收敛性。 ASEEGA似乎是诊断辅助和自动救护车评分的理想选择。