Epileptic EEG signal recognition is an important method for epilepsy detection. In essence, epileptic EEG signal recognition is a typical imbalanced classification task. However, traditional machine learning methods used for imbalanced epileptic EEG signal recognition face many challenges: (1) traditional machine learning methods often ignore the imbalance of epileptic EEG signals, which leads to misclassification of positive samples and may cause serious consequences and (2) the existing imbalanced classification methods ignore the interrelationship between samples, resulting in poor classification performance. To overcome these challenges, a graph-based extreme learning machine method (G-ELM) is proposed for imbalanced epileptic EEG signal recognition. The proposed method uses graph theory to construct a relationship graph of samples according to data distribution. Then, a model combining the relationship graph and ELM is constructed; it inherits the rapid learning and good generalization capabilities of ELM and improves the classification performance. Experiments on a real imbalanced epileptic EEG dataset demonstrated the effectiveness and applicability of the proposed method.

中文翻译：

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基于图的极限学习机识别失衡癫痫脑电图信号

癫痫脑电信号识别是癫痫检测的重要方法。本质上，癫痫脑电信号识别是一个典型的不平衡分类任务。然而，用于不平衡癫痫脑电信号识别的传统机器学习方法面临许多挑战：（1）传统机器学习方法往往忽略癫痫脑电信号的不平衡，导致正样本错误分类并可能造成严重后果；（2）现有的不平衡分类方法忽略了样本之间的相互关系，导致分类性能不佳。为了克服这些挑战，提出了一种基于图的极限学习机方法（G-ELM）用于不平衡癫痫脑电信号识别。该方法利用图论根据数据分布构建样本关系图。然后，构建一个结合关系图和ELM的模型；它继承了ELM的快速学习和良好的泛化能力，提高了分类性能。对真实不平衡癫痫脑电图数据集的实验证明了所提出方法的有效性和适用性。