Ikelos-RWA。无张力快速眼动睡眠自动量化工具的验证。

IF 1.6 4区 医学 Q3 CLINICAL NEUROLOGY Clinical EEG and Neuroscience Pub Date : 2024-11-01 Epub Date: 2023-05-16 DOI:10.1177/15500594231175320
Alexandra Papakonstantinou, Jannis Klemming, Martin Haberecht, Dieter Kunz, Frederik Bes
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引用次数: 0

摘要

研究的目标。提出并评估一种自动评分算法,用于量化快速眼动睡眠行为障碍(RBD)患者无张力快速眼动睡眠(RWA),该算法基于一种普遍接受的、经过良好验证的视觉评分方法(“Montreal”相位和张力)和一种最近开发的、简洁的评分方法(Ikelos-RWA)。方法。回顾性分析20例RBD患者(68.2±7.2岁)和对照组20例周期性肢体运动障碍患者(65.9±6.7岁)的视频多导睡眠图。通过快速眼动睡眠时的下巴肌电图估计RWA。视觉和自动RWA评分是相关的,并计算了RBD患者1735分钟快速眼动睡眠的一致性(a)和Cohen’s Kappa (k)。采用受试者工作特征(ROC)分析评价鉴别效果。然后将该算法应用于232名RBD患者的多导睡眠图(分析的快速眼动睡眠总时间:17,219分钟)并进行评估,同时将不同的输出参数相关联。结果。视觉和计算机衍生RWA评分显著相关(蒙特利尔:rTM = 0.77;相位蒙特利尔:rPM = 0.78;Ikelos-RWA: rI = 0.97;所有p kTM = 0.71;kPM = 0.79;kI = 0.77)。ROC分析结果显示,最佳操作点的灵敏度为95% ~ 100%,特异度为84% ~ 95%,曲线下面积(AUC)为0.98,判别能力强。232例患者自动RWA评分相关性显著(rTM{I} = 0.95;rPM{I} = 0.91, p提出的算法是一种易于使用和有效的工具,用于RBD患者的RWA自动评分,并且可能被证明是有效的,可以公开使用。
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Ikelos-RWA. Validation of an Automatic Tool to Quantify REM Sleep Without Atonia.

Study Objectives. To present and evaluate an automatic scoring algorithm for quantification of REM-sleep without atonia (RWA) in patients with REM-sleep behaviour disorder (RBD) based on a generally accepted, well-validated visual scoring method, ("Montreal" phasic and tonic) and a recently developed, concise scoring method (Ikelos-RWA). Methods. Video-polysomnographies of 20 RBD patients (68.2 ± 7.2 years) and 20 control patients with periodic limb movement disorder (65.9 ± 6.7 years) were retrospectively analysed. RWA was estimated from chin electromyogram during REM-sleep. Visual and automated RWA scorings were correlated, and agreement (a) and Cohen's Kappa (k) calculated for 1735 minutes of REM-sleep of the RBD patients. Discrimination performance was evaluated with receiver operating characteristic (ROC) analysis. The algorithm was then applied on the polysomnographies of a cohort of 232 RBD patients (total analysed REM-sleep: 17,219 minutes) and evaluated, while correlating the different output parameters. Results. Visual and computer-derived RWA scorings correlated significantly (tonic Montreal: rTM = 0.77; phasic Montreal: rPM = 0.78; Ikelos-RWA: rI = 0.97; all p < 0.001) and showed good to excellent Kappa coefficients (kTM = 0.71; kPM = 0.79; kI = 0.77). The ROC analysis showed high sensitivities (95%-100%) and specificities (84%-95%) at the optimal operation points, with area under the curve (AUC) of 0.98, indicating high discriminating capacity. The automatic RWA scorings of 232 patients correlated significantly (rTM{I} = 0.95; rPM{I} = 0.91, p < 0.0001). Conclusions. The presented algorithm is an easy-to-use and valid tool for automatic RWA scoring in patients with RBD and may prove effective for general use being publicly available.

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来源期刊
Clinical EEG and Neuroscience
Clinical EEG and Neuroscience 医学-临床神经学
CiteScore
5.20
自引率
5.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Clinical EEG and Neuroscience conveys clinically relevant research and development in electroencephalography and neuroscience. Original articles on any aspect of clinical neurophysiology or related work in allied fields are invited for publication.
期刊最新文献
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