• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Clinical Neurophysiology >Detecting epileptic seizures in long-term human EEG: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns.
【24h】

Detecting epileptic seizures in long-term human EEG: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns.

机译：检测长期人类脑电图中的癫痫发作：自动在线和实时检测和多态性癫痫发作模式分类的新方法。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Epileptic seizures can cause a variety of temporary changes in perception and behavior. In the human EEG they are reflected by multiple ictal patterns, where epileptic seizures typically become apparent as characteristic, usually rhythmic signals, often coinciding with or even preceding the earliest observable changes in behavior. Their detection at the earliest observable onset of ictal patterns in the EEG can, thus, be used to start more-detailed diagnostic procedures during seizures and to differentiate epileptic seizures from other conditions with seizure-like symptoms. Recently, warning and intervention systems triggered by the detection of ictal EEG patterns have attracted increasing interest. Since the workload involved in the detection of seizures by human experts is quite formidable, several attempts have been made to develop automatic seizure detection systems. So far, however, none of these found widespread application. Here, we present a novel procedure for generic, online, and real-time automatic detection of multimorphologic ictal-patterns in the human long-term EEG and its validation in continuous, routine clinical EEG recordings from 57 patients with a duration of approximately 43 hours and additional 1,360 hours of seizure-free EEG data for the estimation of the false alarm rates. We analyzed 91 seizures (37 focal, 54 secondarily generalized) representing the six most common ictal morphologies (alpha, beta, theta, and delta- rhythmic activity, amplitude depression, and polyspikes). We found that taking the seizure morphology into account plays a crucial role in increasing the detection performance of the system. Moreover, besides enabling a reliable (mean false alarm rate<0.5/h, for specific ictal morphologies<0.25/h), early and accurate detection (average correct detection rate>96%) within the first few seconds of ictal patterns in the EEG, this procedure facilitates the automatic categorization of the prevalent seizure morphologies without the necessity to adapt the proposed system to specific patients.
机译：癫痫发作可引起感知和行为的各种暂时变化。在人脑电图中，它们由多种发作模式反映，癫痫性发作通常表现为特征性的，通常是节律性信号，通常与最早可观察到的行为变化相吻合甚至早于可观察到的行为变化。因此，在最早可观察到的脑电图发作模式发作时对其进行检测，可用于在癫痫发作期间开始更详细的诊断程序，并将癫痫性癫痫发作与其他具有癫痫样症状的病症区分开。最近，由短波脑电图模式检测触发的预警和干预系统引起了越来越多的兴趣。由于人类专家检测癫痫发作的工作量非常大，因此已经进行了数种尝试来开发自动癫痫发作检测系统。但是，到目前为止，这些方法中没有一个得到广泛应用。在这里，我们提出了一种新的程序，用于对人类长期脑电图的多形态眼动模式进行通用，在线和实时自动检测，并在连续，常规的临床脑电图记录中对57名患者进行了验证，持续时间约43小时以及额外的1,360小时无癫痫发作的EEG数据，用于估计误报率。我们分析了代表六种最常见的发作形态（α，β，θ和Δ节律性活动，振幅下降和多峰）的91次发作（37次发作，其次54次）。我们发现考虑到癫痫发作形态在提高系统的检测性能中起着至关重要的作用。此外，除了能够提供可靠的（平均误报率<0.5 / h，对于特定的发作形态<0.25 / h）之外，还可以在EEG的发作模式的前几秒钟内进行早期而准确的检测（平均正确检测率> 96％） ，此程序有助于对流行的癫痫发作形态进行自动分类，而无需使建议的系统适应特定患者。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号