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首页> 外文期刊>Epilepsia: Journal of the International League against Epilepsy >Novel features for capturing temporal variations of rhythmic limb movement to distinguish convulsive epileptic and psychogenic nonepileptic seizures
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Novel features for capturing temporal variations of rhythmic limb movement to distinguish convulsive epileptic and psychogenic nonepileptic seizures

机译:捕获节奏肢体运动时间变化的新功能,以区分惊厥性癫痫和心理注意力癫痫发作

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摘要

Objective To investigate the characteristics of motor manifestation during convulsive epileptic and psychogenic nonepileptic seizures (PNES), captured using a wrist-worn accelerometer (ACM) device. The main goal was to find quantitative ACM features that can differentiate between convulsive epileptic and convulsive PNES. Methods In this study, motor data were recorded using wrist-worn ACM-based devices. A total of 83 clinical events were recorded: 39 generalized tonic-clonic seizures (GTCS) from 12 patients with epilepsy, and 44 convulsive PNES from 7 patients (one patient had both GTCS and PNES). The temporal variations in the ACM traces corresponding to 39 GTCS and 44 convulsive PNES events were extracted using Poincare maps. Two new indices-tonic index (TI) and dispersion decay index (DDI)-were used to quantify the Poincare-derived temporal variations for every GTCS and convulsive PNES event. Results The TI and DDI of Poincare-derived temporal variations for GTCS events were higher in comparison to convulsive PNES events (P 0.001). The onset and the subsiding patterns captured by TI and DDI differentiated between epileptic and convulsive nonepileptic seizures. An automated classifier built using TI and DDI of Poincare-derived temporal variations could correctly differentiate 42 (sensitivity: 95.45%) of 44 convulsive PNES events and 37 (specificity: 94.87%) of 39 GTCS events. A blinded review of the Poincare-derived temporal variations in GTCS and convulsive PNES by epileptologists differentiated 26 (sensitivity: 70.27%) of 44 PNES events and 33 (specificity: 86.84%) of 39 GTCS events correctly. Significance In addition to quantifying the motor manifestation mechanism of GTCS and convulsive PNES, the proposed approach also has diagnostic significance. The new ACM features incorporate clinical characteristics of GTCS and PNES, thus providing an accurate, low-cost, and practical alternative to differential diagnosis of PNES.
机译:目的探讨使用腕带加速度计(ACM)装置捕获的惊厥癫痫和心动注意到(PNES)的运动表现的特征。主要目标是找到可以区分抽吸性癫痫和抽搐潘纳之间的定量ACM功能。方法在本研究中,使用基于手腕磨损的ACM的设备记录电机数据。记录了83例临床事件:39例癫痫患者的推广滋补克隆癫痫发作(GTCS),7名患者的44名痉挛性肺毒素(一名患者有GTCS和PNES)。使用Poincare Maps提取对应于39个GTCS和44个痉挛的PNES事件的ACM迹线的时间变化。两个新的索引 - 补品索引(TI)和色散衰减索引(DDI) - 用于量化每个GTCS和痉挛的PNES事件的Poincare-Solived时间变化。结果与痉挛的潘纳事件相比,GTCS事件的Poincare衍生的时间变化的Ti和DDI较高(P <0.001)。 Ti和DDI捕获的发病和置位模式的癫痫和痉挛非分泌癫痫发作。使用TI和DDI建造的自动分类器可以正确地区分42(灵敏度:95.45%)44个惊厥性缺点事件和37个(特异性:94.87%)39个GTCS事件。通过脱孔医生分化为44吨事件和33个(特异性:86.84%)的庞大的GTCS源于GTCS的时间变异,并通过嗜血学,分化为44吨,33种(特异性:86.84%),39种GTCS事件。除了量化GTCS的电动机表现机制和痉挛性PNES之外的重要性,所提出的方法还具有诊断意义。新的ACM特征包括GTCS和PNES的临床特征,从而提供了精确,低成本,实际的差异诊断型普利斯。

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