Automatic Identification of Spike-Wave Events and Non-Convulsive Seizures with a Reduced Set of Electrodes
Automatic Identification of Spike-Wave Events and Non-Convulsive Seizures with a Reduced Set of Electrodes
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使用减少的电极组自动识别尖波事件和非惊厥性癫痫发作
DOI：
10.1109/iembs.2007.4352694
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
E. John
中科院分区：
文献类型：
--
作者：
A. Jacquin;E. Causevic;E. John
大脑中的癫痫样活动，无论是局限性的还是全身性的，都是异常脑电的重要类别。癫痫发作是由阵发性大脑活动引起的相对短暂的精神、运动或感觉活动障碍的发作。它们并不总是伴随着我们通常将其与癫痫这个词联系在一起的特有的抽搐。在这种情况下，可将其称为非抽搐癫痫持续状态(NCSE)[1]或失神发作(以前称为“小发作”发作)。在头皮记录的脑电中，它们通常表现为大幅度的棘波“模式”(或“事件”)，通常是以突发形式出现的。如果没有发现和治疗，它们可能会导致严重的大脑和行为功能障碍，干扰信息处理，或以其他方式导致精神状态改变。在这篇文章中，我们描述了一种在BrainScope_ED原型仪器中实现的算法，该仪器旨在对在急诊科(ED)或其他临床环境中检测到的癫痫发作发出警报。BrainScope_ED使用精简的电极组(8个而不是19个)。提出的信号处理算法基于对EEG信号进行小波分析获得的棘波事件的检测，并结合使用分维估计对EEG的复杂性进行分析。结果表明，该算法具有良好的灵敏度和特异度。特别是，分形分析是去除错误检测的棘波事件(假阳性)的关键因素，这些事件可能是由诸如快速眼皮颤动之类的自愿或非自愿伪影引起的。
Epileptiform activity in the brain, whether localized or generalized, constitutes an important category of abnormal electroencephalogram (EEG). Seizures are episodes of relatively brief disturbances of mental, motor or sensory activity caused by paroxysmal cerebral activity. They are not always accompanied by the characteristic convulsions that we commonly associate with the word epilepsy. In this case, they may be referred to as non-convulsive status epilepticus (NCSE) [1] or as absence seizures (formerly called "petit mal" seizures). They often manifest themselves in scalp-recorded EEG as large-amplitude spike-wave "patterns" (or "events"), usually occurring in bursts. If left undetected and untreated, they can potentially cause significant brain and behavioral dysfunctions, interfere with information processing, or otherwise contribute to altered mental status. In this paper, we describe an algorithm to be implemented in a prototype BrainScope_ED instrument meant to alert to a detected seizure in an emergency department (ED) or other clinical setting. BrainScope_ED uses a reduced electrode set (8 instead of 19). The proposed signal processing algorithm is based on the detection of spike-wave events obtained from a wavelet analysis of the EEG signal, combined with an analysis of the complexity of the EEG using fractal dimension estimates. We show that this algorithm has excellent sensitivity and specificity. In particular, the fractal analysis is a key factor in the removal of falsely detected spike-wave events (false positives) that can be caused by voluntary or involuntary artifacts such as fast eyelid flutter.