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自动区分癫痫性和非癫痫性惊厥发作。

Automated differentiation between epileptic and nonepileptic convulsive seizures.

机构信息

Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark; Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.

出版信息

Ann Neurol. 2015 Feb;77(2):348-51. doi: 10.1002/ana.24338. Epub 2015 Jan 17.

Abstract

Our objective was the clinical validation of an automated algorithm based on surface electromyography (EMG) for differentiation between convulsive epileptic and psychogenic nonepileptic seizures (PNESs). Forty-four consecutive episodes with convulsive events were automatically analyzed with the algorithm: 25 generalized tonic-clonic seizures (GTCSs) from 11 patients, and 19 episodes of convulsive PNES from 13 patients. The gold standard was the interpretation of the video-electroencephalographic recordings by experts blinded to the EMG results. The algorithm correctly classified 24 GTCSs (96%) and 18 PNESs (95%). The overall diagnostic accuracy was 95%. This algorithm is useful for distinguishing between epileptic and psychogenic convulsive seizures.

摘要

我们的目的是对一种基于表面肌电图(EMG)的自动算法进行临床验证,以区分癫痫性抽搐发作和心因性非癫痫性发作(PNES)。对 44 次伴有抽搐事件的连续发作进行了自动分析:11 名患者的 25 次全面强直-阵挛发作(GTCSs)和 13 名患者的 19 次抽搐性 PNES 发作。金标准是对视频-脑电图记录的解释,由对 EMG 结果不知情的专家进行。该算法正确分类了 24 次 GTCSs(96%)和 18 次 PNESs(95%)。总体诊断准确率为 95%。该算法有助于区分癫痫性和心因性抽搐发作。

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