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首页> 外文期刊>The Canadian Journal of Neurological Sciences: le Journal Canadien des Sciences Neurologiques >Identification of the temporal components of seizure onset in the scalp EEG.
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Identification of the temporal components of seizure onset in the scalp EEG.

机译：识别癫痫发作在头皮脑电图中的时间性成分。

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BACKGROUND: The identification of the earliest indication of rhythmical oscillations and paroxysmal events associated with an epileptic seizure is paramount in identifying the location of the seizure onset in the scalp EEG. In this work, data-dependent filters are designed that can help reveal obscure activity at the onset of seizures in problematic EEGs. METHODS: Data-dependent filters were designed using temporal patterns common to selected segments from pre-ictal and ictal portions of the scalp EEG. Temporal patterns that accounted for more variance in the ictal segment than in the pre-ictal segment of the scalp EEG were used to form the filters. RESULTS: Application of the filters to the scalp EEG revealed temporal components in the seizure onset in the scalp recording that were not obvious in the unfiltered EEG. Examination of the filtered EEG enabled the onset of the seizure to be recognized earlier in the recording. The utility of the filters was confirmed qualitatively by comparing the scalp recording to the intracranial recording and quantitatively by calculating correlation coefficients between the scalp and intracranial recordings before and after filtering. CONCLUSION: The data-dependent approach to EEG filter design allows automatic detection of the basic frequencies present in the seizure onset. This approach is more effective than narrow band-pass filtering for eliminating artifactual and other interference that can obscure the onset of a seizure. Therefore, temporal-pattern filtering facilitates the identification of seizure onsets in challenging scalp EEGs.
机译：背景：与癫痫性癫痫发作相关的节律性振荡和阵发性事件的最早迹象的识别对于识别癫痫发作在头皮脑电图中的位置至关重要。在这项工作中，设计了依赖数据的过滤器，可以帮助发现有问题的脑电图发作时隐匿的活动。方法：使用从头皮脑电图的发作前和发作期部分中选择的节段共有的时间模式设计依赖数据的滤波器。在头皮EEG中比头皮EEG的前期部分占更多差异的时间模式被用来形成过滤器。结果：将过滤器应用于头皮脑电图显示，头皮记录中癫痫发作的时间成分在未过滤的脑电图中不明显。对过滤后的脑电图的检查可以使癫痫发作在记录中更早地被识别出来。通过将头皮记录与颅内记录进行比较，并通过计算过滤前后头皮与颅内记录之间的相关系数来定量地确定过滤器的实用性。结论：脑电滤波器设计的数据依赖方法可以自动检测癫痫发作中的基本频率。这种方法比窄带通滤波更有效，可以消除可能掩盖癫痫发作的人为干扰和其他干扰。因此，时间模式过滤有助于在具有挑战性的头皮脑电图中识别癫痫发作。

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