Department of Neurology, General Hospital Hietzing With Neurological Center Rosenhügel, Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Medical Faculty, Sigmund Freud University, Vienna, Austria.
Brain Sentinel, Inc., San Antonio, TX, United States.
Seizure. 2021 Mar;86:52-59. doi: 10.1016/j.seizure.2020.11.013. Epub 2020 Nov 23.
Accurate characterization and quantification of seizure types are critical for optimal pharmacotherapy in epilepsy patients. Technological advances have made it possible to continuously monitor physiological signals within or outside the hospital setting. This study tested the utility of single-channel surface-electromyography (sEMG) for characterization of motor epileptic seizure semiology.
Seventy-one subjects were prospectively enrolled where vEEG and sEMG were simultaneously recorded. Three epileptologists independently identified and classified seizure events with upper-extremity (UE) motor activity by reviewing vEEG, serving as a clinical standard. Surface EMG recorded during the events identified by the clinical standard were evaluated using automated classification methods and expert review by a second group of three independent epileptologists (blinded to the vEEG data). Surface EMG classification categories included: tonic-clonic (TC), tonic only, clonic only, or other motor seizures. Both automated and expert review of sEMG was compared to the clinical standard.
Twenty subjects experienced 47 motor seizures. Automated sEMG event classification methods accurately classified 72 % (95 % CI [0.57, 0.84]) of events (15/18 TC seizures, 5/9 tonic seizures, 1/3 clonic seizures, and 13/17 other seizures). Three independent reviewers' majority-rule analysis of sEMG correctly classified 81 % (95 % CI [0.67, 0.91]) of events (16/18 TC seizures, 8/9 tonic seizures, 1/3 clonic seizures, and 13/17 other manifestations).
Continuous monitoring of sEMG data provides an objective measure to evaluate motor seizure activity. Using sEMG from a wearable monitor recorded from the biceps, automated and expert review may be used to characterize the semiology of events with UE motor activity, particularly TC and tonic seizures.
准确描述和量化癫痫发作类型对于癫痫患者的最佳药物治疗至关重要。技术进步使得在医院内外连续监测生理信号成为可能。本研究测试了单通道表面肌电图(sEMG)用于描述运动性癫痫发作症状学的效用。
71 名受试者前瞻性入组,同时记录 vEEG 和 sEMG。三位癫痫专家通过回顾 vEEG 独立识别和分类具有上肢(UE)运动活动的发作事件,作为临床标准。使用自动分类方法和由另外三位独立癫痫专家(对 vEEG 数据不知情)进行的专家审查评估在临床标准识别的事件期间记录的表面 EMG。表面 EMG 分类类别包括:强直-阵挛(TC)、单纯强直、单纯阵挛或其他运动性发作。自动和专家对 sEMG 的审查均与临床标准进行了比较。
20 名受试者经历了 47 次运动性发作。自动 sEMG 事件分类方法准确地分类了 72%(95%CI[0.57,0.84])的事件(15/18 TC 发作,5/9 强直发作,1/3 阵挛发作和 13/17 其他发作)。三位独立审查员的 sEMG 多数规则分析正确分类了 81%(95%CI[0.67,0.91])的事件(16/18 TC 发作,8/9 强直发作,1/3 阵挛发作和 13/17 其他表现)。
连续监测 sEMG 数据提供了评估运动性发作活动的客观测量方法。使用从二头肌记录的可穿戴式监测器的 sEMG,自动和专家审查可用于表征具有 UE 运动活动的事件的症状学,特别是 TC 和强直发作。