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定制多导睡眠图系统中腿部运动计数及周期性腿部运动分析的验证

Validation of a leg movements count and periodic leg movements analysis in a custom polysomnography system.

作者信息

Stefani Ambra, Heidbreder Anna, Hackner Heinz, Högl Birgit

机构信息

Department of Neurology, Medical University of Innsbruck, Anichstrasse 35, Innsbruck, A-6020, Austria.

出版信息

BMC Neurol. 2017 Feb 23;17(1):42. doi: 10.1186/s12883-017-0821-6.

DOI:10.1186/s12883-017-0821-6
PMID:28231845
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5324307/
Abstract

BACKGROUND

Periodic leg movements (PLM) during sleep (PLMS) are considered strongly related to restless legs syndrome (RLS), and are associated with polymorphisms in RLS risk genes. Various software for automatic analysis of PLMS are available, but only few of them have been validated. Aim of this study was to validate a leg movements count and analysis integrated in a commercially available polysomnography (PSG) system against manual scoring.

METHODS

Twenty RLS patients with a PLMS index > 20/h and 20 controls with a PLMS index < 5/h were included. Manual and computerized scoring of leg movements (LM) and PLM was performed according to the standard American Academy of Sleep Medicine (AASM) criteria. LM and PLM indices during sleep and wakefulness, the rate of PLMS associated with respiratory events, intermovement interval and periodicity indices were manually and automatically scored.

RESULTS

The correlation between manual and computerized scoring was high for all investigated parameters (Spearman correlation coefficients 0.751-0.996, p < 0.001; intraclass correlation coefficients 0.775-0.999, p < 0.001). Bland-Altman plots showed high agreement between manual and automatic analysis.

CONCLUSIONS

This study validated an automatic LM count and PLM analysis against the gold standard manual scoring according to AASM criteria. The data demonstrate that the software used in this study has an outstanding performance for computerized LM and PLM scoring, and LM and PLM indices generated with this software can be reliably integrated in the routine PSG report. This automatic analysis is also an excellent tool for research purposes.

摘要

背景

睡眠期周期性腿部运动（PLMS）被认为与不宁腿综合征（RLS）密切相关，且与RLS风险基因的多态性有关。有多种用于自动分析PLMS的软件，但只有少数经过了验证。本研究的目的是针对人工评分，验证一种集成在商用多导睡眠图（PSG）系统中的腿部运动计数与分析方法。

方法

纳入20例PLMS指数>20次/小时的RLS患者和20例PLMS指数<5次/小时的对照者。根据美国睡眠医学学会（AASM）标准对腿部运动（LM）和PLMS进行人工和计算机评分。对睡眠期和清醒期的LM和PLMS指数、与呼吸事件相关的PLMS发生率、运动间隔时间和周期性指数进行人工和自动评分。

结果

所有研究参数的人工评分与计算机评分之间的相关性都很高（斯皮尔曼相关系数为0.751 - 0.996，p<0.001；组内相关系数为0.775 - 0.999，p<0.001）。Bland-Altman图显示人工分析与自动分析之间具有高度一致性。

结论

本研究针对根据AASM标准的金标准人工评分，验证了一种自动LM计数和PLMS分析方法。数据表明，本研究中使用的软件在计算机化LM和PLMS评分方面具有出色的性能，并且用该软件生成的LM和PLMS指数可可靠地整合到常规PSG报告中。这种自动分析也是用于研究目的的优秀工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd87/5324307/28dd980baf07/12883_2017_821_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd87/5324307/d5f867301a26/12883_2017_821_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd87/5324307/af0a5a553750/12883_2017_821_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd87/5324307/28dd980baf07/12883_2017_821_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd87/5324307/d5f867301a26/12883_2017_821_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd87/5324307/af0a5a553750/12883_2017_821_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd87/5324307/28dd980baf07/12883_2017_821_Fig3_HTML.jpg

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