Detecting epileptic seizures in long-term human EEG: A new approach to automatic online and real-time detection and classification of polymorphic seizure patterns
Detecting epileptic seizures in long-term human EEG: A new approach to automatic online and real-time detection and classification of polymorphic seizure patterns
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DOI：
10.1097/wnp.0b013e3181775993
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发表时间：
2008-06-01
影响因子：
2.4
通讯作者：
Aertsen, Ad
中科院分区：
文献类型：
--
作者：
Meier, Ralph;Dittrich, Heike;Aertsen, Ad
癫痫发作可导致各种暂时性的知觉和行为改变。在人类脑电中，它们反映在多种发作模式中，癫痫发作通常作为特征变得明显，通常是有节奏的信号，通常与最早可观察到的行为变化同时发生，甚至先于最早可观察到的行为变化。因此，在EEG中最早可观察到的发作模式的检测可以用于在癫痫发作期间启动更详细的诊断程序，并将癫痫发作与其他具有癫痫样症状的情况区分开来。近年来，由发作期脑电模式检测触发的预警和干预系统引起了越来越多的关注。由于人类专家检测癫痫发作所涉及的工作量相当大，因此已经进行了几次尝试，以开发自动癫痫发作检测系统。然而，到目前为止，这些都没有得到广泛的应用。在这里，我们提出了一种新的方法，用于对人类长期脑电中的多形态发作模式进行通用、在线和实时的自动检测，并在57例患者持续约43小时的连续常规临床脑电记录中进行验证，以及另外1360小时的无癫痫发作的脑电数据，以估计错误警报率。我们分析了91次癫痫发作(37次局灶性发作，54次二次全身性发作)，它们代表了六种最常见的发作形态(阿尔法、贝塔、西塔和增量节律活动、幅度降低和多棘波)。我们发现，考虑到癫痫的形态对于提高系统的检测性能起着至关重要的作用。此外，除了实现可靠的(平均误警率)
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