从长期脑电图数据中区分前期和间期脑状态

Kostas M. Tsiouris, V. Pezoulas, D. Koutsouris, M. Zervakis, D. Fotiadis
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引用次数: 28

摘要

脑电图信号的前状态判别在神经科学中具有重要意义,在癫痫发作预测领域尚未提供确凿的证据。在这项研究中,研究了三种不同的分类方法,包括重复增量修剪产生误差减少(RIPPER)算法,支持向量机(svm)和神经网络(nn),以研究它们区分预测和间期脑电片段的能力。利用公开的EEG数据,从每个片段中提取广泛的特征,然后应用到分类器中。该分析包括针对患者个体的方法,以优化每个患者的决策,以及针对患者独立的方法,以探索一种全局预测方法,可以从所有患者中随机选择牙周和牙周段进行区分。总的来说,第一种方法旨在揭示患者特定的癫痫特征,而第二种方法寻求潜在的一般癫痫相关迹象。结果表明,在患者特异性病例中,SVM分类器在前、间两类中均表现出最高的分类准确率,灵敏度和特异度均达到85.75%。正如预期的那样,由于直肠活动的复杂性和患者病情的差异,患者独立病例的分类性能较低,为68.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discrimination of Preictal and Interictal Brain States from Long-Term EEG Data
The discrimination of the preictal state in EEG signals is of great importance in neuroscience and the epileptic seizure prediction field has yet to provide conclusive evidence. In this study, three different classification approaches, including the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm, Support Vector Machines (SVMs) and Neural Networks (NNs), are investigated for their ability to discriminate preictal from interictal EEG segments. Using public EEG data, a wide range of features is extracted from each segment and then applied to the classifiers. The analysis covers a patient-specific approach, so as to optimize the decision to each patient individually and a patient-independent approach in order to explore a global prediction approach that can discriminate randomly selected preictal and interictal segments from all patients. Overall, the first approach aims at revealing patient-specific epileptic characteristics, whereas the second seeks for potential general preictal-related signs. The results reveal that in the patient-specific case, the SVM classifier exhibits the highest classification accuracy in both preictal and interictal classes reaching 85.75% sensitivity and specificity. As it is expected, the classification performance is lower for the patient-independent case at 68.5%, due to the complicated nature of preictal activity and the variations among patients condition.
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