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不安腿综合征和睡眠期周期性腿部运动患者夜间腿部运动活动的计算机辅助检测。

Computer-assisted detection of nocturnal leg motor activity in patients with restless legs syndrome and periodic leg movements during sleep.

作者信息

Ferri Raffaele, Zucconi Marco, Manconi Mauro, Bruni Oliviero, Miano Silvia, Plazzi Giuseppe, Ferini-Strambi Luigi

机构信息

Sleep Research Centre, Department of Neurology I.C., Oasi Institute (IRCCS), Troina, Italy.

出版信息

Sleep. 2005 Aug 1;28(8):998-1004. doi: 10.1093/sleep/28.8.998.

Abstract

STUDY OBJECTIVES

To assess the performance of a new method for automatic detection of periodic leg movements during sleep.

METHODS

Leg movements during sleep were visually detected in the tibialis anterior muscles recordings of 15 patients with restless legs syndrome and 15 normal controls. Leg movements were detected automatically by means of a new computer method with which electromyogram signals are first digitally band-pass filtered and then rectified; subsequently, the detection of leg movements is performed by using 2 thresholds: one for the starting point and another to detect the end point of each leg movement. Sensitivity and false-positive rate were obtained; the American Sleep Disorders Association parameters were also computed, and the results analyzed by means of the Kendall W coefficient, the linear correlation coefficient and the Bland-Altman plots.

SETTING

N/A.

PARTICIPANTS

Fifteen patients with restless legs syndrome and periodic leg movements and 15 controls.

MEASUREMENTS AND RESULTS

High values of the Kendall W coefficient of concordance between automatic and visual analysis were found with values close to 1 and the linear correlation coefficient for leg movements index and total leg movements index was > 0.950 (p < .000001). The Bland-Altman plots provided the limits of agreement between visual and computer detection, which were -9.01 and +9.89 for the periodic leg movement index. None of the normal controls was found to have periodic leg movement indexes >5 after automatic analysis.

CONCLUSIONS

Our method can be applied to the clinical evaluation of periodic leg movements during sleep, with some caution in patients with a low periodic leg movement indexes. Large-scale research application is possible and can be considered as reliable.

摘要

研究目的

评估一种睡眠期间周期性腿部运动自动检测新方法的性能。

方法

在15例不宁腿综合征患者和15例正常对照者的胫前肌记录中,通过视觉检测睡眠期间的腿部运动。采用一种新的计算机方法自动检测腿部运动,该方法首先对肌电图信号进行数字带通滤波,然后进行整流;随后,使用两个阈值检测腿部运动:一个用于起始点,另一个用于检测每个腿部运动的终点。获得灵敏度和假阳性率;还计算了美国睡眠障碍协会的参数,并通过肯德尔W系数、线性相关系数和布兰德-奥特曼图对结果进行分析。

设置

无。

参与者

15例患有不宁腿综合征和周期性腿部运动的患者以及15例对照者。

测量与结果

自动分析与视觉分析之间的肯德尔W一致性系数值较高,接近1,腿部运动指数和总腿部运动指数的线性相关系数>0.950(p<.000001)。布兰德-奥特曼图给出了视觉检测与计算机检测之间的一致性界限,周期性腿部运动指数的界限为-9.01和+9.89。自动分析后,未发现正常对照者的周期性腿部运动指数>5。

结论

我们的方法可应用于睡眠期间周期性腿部运动的临床评估,对于周期性腿部运动指数较低的患者需谨慎使用。大规模研究应用是可行的,可认为是可靠的。

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