Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark; Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.
Ann Neurol. 2015 Feb;77(2):348-51. doi: 10.1002/ana.24338. Epub 2015 Jan 17.
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.
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。