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Use of electrographic seizures and interictal epileptiform discharges for improving performance in seizure prediction

机译:使用电子癫痫发作和发作间期癫痫样放电改善癫痫发作预测表现

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Electroencephalography (EEG) is an important tool in analyzing brain activity. EEG recording is effectively used for detection and prediction of electrophysiological abnormalities due to epilepsy. Epileptic seizure is a brain disorder which affects the patients acutely. Seizures are controllable with medication in 70% of the cases, however the rest may continue to have recurring epileptic seizures despite medications. Since seizures are unpredictable clinically, these patients will also live with perpetual anxiety about the onset of seizure, apart from being affected by the seizure consequences such as drowsiness, headache, vomiting, etc. The seizures can cause injury to the patients, and in some cases may even result in death. Seizure prediction can aid patients with disabling seizure by detecting the seizure precursors in advance and alerting the patients or their caregivers. If the seizure is predicted in advance it can be aborted by fast acting Anti-epileptic drugs (AEDs) or other treatment procedures. This will also aid pre-surgical video EEG monitoring wherein prediction of the ictal onset zone is paramount and machine alarms can be devised. In this paper, we are comparing the results of our research work related to the seizure prediction models. First model, as in usual practice, differentiates between preictal and interictal data segments only. The other seizure prediction model uses Interictal Epileptiform Discharges (IEDs), Electrographic Seizures (ES) and ictal data segments in addition to the first model. We found that the latter one provided better results and improved the seizure prediction performance.
机译:脑电图(EEG)是分析大脑活动的重要工具。脑电图记录可有效地用于检测和预测由癫痫引起的电生理异常。癫痫病发作是一种会严重影响患者的脑部疾病。在70%的病例中,药物治疗可控制癫痫发作,但是尽管有药物治疗,其余的癫痫发作仍可能继续发作。由于癫痫发作在临床上是不可预测的,因此,这些患者除了会受到嗜睡,头痛,呕吐等癫痫发作后果的影响外,还会对癫痫发作永久感到焦虑。癫痫发作会对患者造成伤害,在某些情况下甚至可能导致死亡。癫痫发作预测可通过提前检测癫痫发作的前兆并提醒患者或其护理人员来帮助禁用癫痫发作。如果提前预计会发作,可以通过速效抗癫痫药(AED)或其他治疗程序中止。这也将有助于进行术前视频EEG监测,其中对发作期区域的预测至关重要,并且可以设计出机器警报。在本文中,我们正在比较与癫痫发作预测模型有关的研究结果。与通常的做法一样,第一个模型仅区分发作前和发作间的数据段。除第一个模型外,其他癫痫发作预测模型还使用了发作间癫痫样放电(IED),电描记性癫痫发作(ES)和发作数据段。我们发现,后者提供了更好的结果并改善了癫痫发作的预测性能。

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