验证 REM 睡眠无张力的半自动化评分在 RBD 患者中的应用。

Validation of semiautomatic scoring of REM sleep without atonia in patients with RBD.

机构信息

Department of Trauma, Hand and Reconstructive Surgery, University Hospital Hamburg Eppendorf, UKE, Martinistr. 52, 20246 Hamburg, Germany.

Hephata Klinik 34613 Schwalmstadt Schimmelpfengstr. 6 Germany; Philipps University Marburg, Department of Neurology, Baldinger Str. 35043 Marburg, Germany.

出版信息

Sleep Med. 2018 Jun;46:107-113. doi: 10.1016/j.sleep.2018.03.010. Epub 2018 Mar 31.

DOI:10.1016/j.sleep.2018.03.010

PMID:29773203

Abstract

OBJECTIVE/BACKGROUND: To evaluate REM sleep without atonia (RSWA) in REM sleep behavior disorder (RBD) several automatic algorithms have been developed. We aimed to validate our algorithm (Mayer et al., 2008) in order to assess the following: (1). capability of the algorithm to differentiate between RBD, night terror (NT), somnambulism (SW), Restless legs syndrome (RLS), and obstructive sleep apnea (OSA), (2). the cut-off values for short (SMI) and long muscle activity (LMI), (3). which muscles qualify best for differential diagnosis, and (4). the comparability of RSWA and registered movements between automatic and visual analysis of videometry.

PATIENTS/METHODS: RSWA was automatically scored according to Mayer et al., 2008 in polysomnographies of 20 RBD, 10 SW/NT, 10 RLS and 10 OSA patients. Receiver operating characteristic (ROC) curves were used to determine the sensitivity and specificity of SMI and LMI. Independent samples were calculated with t-tests. Boxplots were used for group comparison. The comparison between motor events by manual scoring and automatic analysis were performed with "Visual Basic for Applications" (VBA) for every hundredth second.

RESULTS

Our method discriminates RBD from SW/NT, OSA and RLS with a sensitivity of 72.5% and a specificity of 86.7%. Automatic scoring identifies more movements than visual video scoring. Mentalis muscle discriminates the sleep disorders best, followed by FDS, which was only recorded in SW/NT. Cut-off values for RSWA are comparable to those found by other groups.

CONCLUSION

The semi-automatic RSWA scoring method is capable to confirm RBD and to discriminate it with moderate sensitivity from other sleep disorders.

摘要

目的/背景：为了评估 REM 睡眠无动（RSWA）在 REM 睡眠行为障碍（RBD）中的作用，已经开发了几种自动算法。我们旨在验证我们的算法（Mayer 等人，2008 年），以评估以下内容：（1）该算法区分 RBD、夜惊（NT）、梦游（SW）、不宁腿综合征（RLS）和阻塞性睡眠呼吸暂停（OSA）的能力，（2）短肌活动（SMI）和长肌活动（LMI）的截断值，（3）最适合鉴别诊断的肌肉，以及（4）自动分析和视频分析之间 RSWA 和记录运动的可比性。

患者/方法：根据 Mayer 等人，2008 年的标准，对 20 例 RBD、10 例 SW/NT、10 例 RLS 和 10 例 OSA 患者的多导睡眠图进行了自动 RSWA 评分。使用受试者工作特征（ROC）曲线确定 SMI 和 LMI 的灵敏度和特异性。使用 t 检验计算独立样本。使用箱线图进行组间比较。使用“应用程序的可视化基础（VBA）”对每百分之一秒的手动评分和自动分析的运动事件进行比较。