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视觉与自动周期性交替模式(CAP)评分:评分者间可靠性研究。

Visual and automatic cyclic alternating pattern (CAP) scoring: inter-rater reliability study.

作者信息

Rosa Agostinho, Alves Gabriela Rodrigues, Brito Magneide, Lopes Maria Cecília, Tufik Sérgio

机构信息

Laboratório de Sistemas Evolutivos e de Engenharia Biomédica, UTL, IST, ISR, Lisboa, Portugal.

出版信息

Arq Neuropsiquiatr. 2006 Sep;64(3A):578-81. doi: 10.1590/s0004-282x2006000400008.

Abstract

The classification of short duration events in the EEG during sleep, as the A stage of the cyclic alternating pattern (CAP) is a tedious and error prone task. The number of events under normal conditions is large (several hundreds), and it is necessary to mark the limits of the events with precision, otherwise the time sensitive classification of the CAP phases (A and B) and specially the scoring of different types of A phases will be compromised. The objective of this study is to verify the feasibility of visual CAP scoring with only one channel of EEG, the evaluation of the inter-scorer agreement in a variety of recordings, and the comparison of the visual scorings with a known automatic scoring system. Sixteen hours of one channel (C4-A1 or C3-A2) of NREM sleep were extracted from eight whole night recordings in European Data Format and presented to the different scorers. The average inter-scorer agreement for all scorers is above 70%, the pair wise inter-scorer agreement found was between 69% up to 77.5%. These values are similar to what has been reported in different type studies. The automatic scoring system has similar performance of the visual scorings. The study also has shown that it is possible to classify the CAP using only one channel of EEG.

摘要

将睡眠期间脑电图(EEG)中的短持续时间事件分类为周期性交替模式(CAP)的A阶段是一项繁琐且容易出错的任务。正常情况下事件数量众多(数百个),必须精确标记事件的界限,否则CAP阶段(A和B)的时间敏感性分类,特别是不同类型A阶段的评分将会受到影响。本研究的目的是验证仅使用单通道EEG进行视觉CAP评分的可行性,评估各种记录中评分者间的一致性,并将视觉评分与已知的自动评分系统进行比较。从八个以欧洲数据格式记录的整晚睡眠记录中提取了16小时的非快速眼动睡眠单通道(C4 - A1或C3 - A2)数据,并呈现给不同的评分者。所有评分者的平均评分者间一致性高于70%,发现的两两评分者间一致性在69%至77.5%之间。这些值与不同类型研究中报告的结果相似。自动评分系统与视觉评分具有相似的性能。该研究还表明,仅使用单通道EEG对CAP进行分类是可行的。

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