An unsupervised method for identifying regions that initiate seizures on intracranial EEG.
An unsupervised method for identifying regions that initiate seizures on intracranial EEG.
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一种无监督的方法，用于识别颅内脑电图引发癫痫发作的区域。
DOI：
10.1109/iembs.2011.6090844
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Litt,Brian
中科院分区：
文献类型：
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作者：
Wulsin,Drausin;Litt,Brian
对药物治疗无反应的癫痫患者目前只有脑外科手术作为主要的替代治疗。因此，确定要切除哪些大脑区域对于医生和患者来说至关重要。目前，这一过程几乎完全是手动的，临床专家和中心之间可能存在很大差异，并且仅依赖于定性EEG特征，所有这些都有助于解释颞外叶癫痫手术的唯一适度成功。在这项研究中，我们探索了一种无监督的，定量的方法来识别癫痫发作的区域。一个高斯混合模型（GMM）被用来聚类500毫秒的颅内脑电图（EEG）的发作前（preictal）和发作期间（intictal）在为期一周的连续记录从三个病人在evalulation癫痫手术。GMM学习范例确定每个患者的最佳聚类数。对于两个患者的时期分为两个集群，我们发现一个集群主要由癫痫发作时期组成，并且通道的一个子集在导致癫痫发作的时间内短暂地“闯入”该集群。这一观察结果与临床假设一致，即某些脑区可能是癫痫发作活动的发起者，我们发现，由医生独立标记为癫痫发作区（SOZ）的通道在统计学上过度表示在癫痫定义的集群中。然而，我们也发现，一个子集的通道没有标记为SOZs有类似的属性标记SOZs。在这项研究中，我们试图避免许多关于什么特征和事件指示癫痫活动的假设，并相信这种分析可以帮助避免手动，非客观的人类SOZ标记的许多陷阱。
Epilepsy patients who do not respond to pharmacological treatments currently have only brain surgery as a major alternative therapy. Identifying which brain areas to remove is thus of critical importance for physicians and the patient. Currently, this process is almost entirely manual, can vary greatly between clinical experts and centers, and depends only on qualitative EEG features, all of which may help explain the only modest success of extratemperal lobe epilepsy surgery. In this study, we explore an unsupervised, quantitative method for identifying seizure onset regions. A Gaussian mixture model (GMM) was used to cluster 500 ms epochs of intracranial electroencephalogram (EEG) prior to (preictal) and during (ictal) seizures in week-long continuous recordings from three patients during evalulation for epilepsy surgery. The GMM learning paradigm determines the optimal number of clusters for each patient. For the two patients whose epochs sorted into two clusters, we found that one cluster was predominantly composed of seizure epochs, and a subset of the channels made brief “forays” into that cluser in the time leading up to seizure onset. This observation is in keeping with the clinical hypothesis that certain brain areas may be the initiators of seizure activity, and we find that the channels independently labeled by physicians as seizure onset zones (SOZs) are statistically overrepesented in the seizure-defined cluster. Nevertheless, we also find that a subset of channels not labeled as SOZs has similar properties as those labeled SOZs. In this study we have tried to avoid many of the assumptions commonly made about what features and events are indicative of epileptogenic activity and believe that such analysis can help avoid many of the pitfalls of manual, non-objective human SOZ marking.