基于图分析的头皮脑电图功能连接网络用于癫痫分类

S. Sargolzaei, M. Cabrerizo, M. Goryawala, A. S. Eddin, M. Adjouadi
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引用次数: 21

摘要

该研究提出了一种全新的全自动数据驱动方法,利用头皮脑电图计算的基于图的功能连接网络来区分癫痫患者和正常对照。从网络图中提取的一组14个密度相关的、基于图距离的和光谱拓扑特征用于分类过程。在对8个受试者进行测试时,该算法的准确率为87.5%,灵敏度为75%,特异性为100%。研究表明,癫痫患者的图基功能连接网络与对照组有显著差异(p<;0.05)。这项研究有可能帮助神经科医生仅根据头皮脑电图做出诊断决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Functional connectivity network based on graph analysis of scalp EEG for epileptic classification
The proposed study presents a novel fully automated data-driven approach for differentiating epileptic subjects from normal controls using graph-based functional connectivity networks calculated using scalp EEG. A set of fourteen density-related, graph distance-based and spectral topological features extracted from the network graph is employed for the classification process. The proposed algorithm demonstrated an accuracy of 87.5% with a sensitivity of 75% and specificity of 100% when tested on 8 subjects. The study showed that graph-based functional connectivity networks in epileptic subjects were significantly different from those of controls (p<;0.05). The study has the potential for aiding neurologists in decision making for diagnostic purposes solely based on scalp EEG.
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