Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is proposed in this paper to improve the identification of seizure events in long-term EEG signals. To better represent the underlying microstructure distributions of EEG signals while preserving the non-stationary nature, discrete wavelet transform (DWT) and uniform 1D-LBP feature extraction procedure are introduced. A learning framework is then designed by the ensemble of weakly trained support vector machines (SVMs). Under-sampling is employed to split the imbalanced seizure and non-seizure samples into multiple balanced subsets where each of them is utilized to train an individual SVM classifier. The weak SVMs are incorporated to build a strong classifier which emphasizes seizure samples and in the meantime analyzing the imbalanced class distribution of EEG data. Final seizure detection results are obtained in a multi-level decision fusion process by considering temporal and frequency factors. The model was validated over two long-term and one short-term public EEG databases. The model achieved a [Formula: see text]-mean of 97.14% with respect to epoch-level assessment, an event-level sensitivity of 96.67%, and a false detection rate of 0.86/h on the long-term intracranial database. An epoch-level [Formula: see text]-mean of 95.28% and event-level false detection rate of 0.81/h were yielded over the long-term scalp database. The comparisons with 14 published methods demonstrated the improved detection performance for imbalanced EEG signals and the generalizability of the proposed model.

中文翻译：

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基于 EasyEnsemble 学习的 EEG 纹理特征和不平衡分类的癫痫发作检测

当非癫痫发作期的持续时间比癫痫发作活动的持续时间长得多时，不平衡数据分类是从脑电图 (EEG) 记录中自动检测癫痫发作的一项具有挑战性的任务。本文提出了一种不平衡学习模型，以改进对长期脑电信号中癫痫发作事件的识别。为了更好地表示脑电信号的底层微结构分布，同时保持非平稳性质，引入了离散小波变换（DWT）和统一的 1D-LBP 特征提取过程。然后由训练有素的支持向量机 (SVM) 的集合设计一个学习框架。欠采样用于将不平衡的癫痫发作和非癫痫发作样本分成多个平衡的子集，其中每个子集都用于训练单个 SVM 分类器。结合弱SVMs构建一个强分类器，强调癫痫发作样本，同时分析EEG数据的不平衡类分布。通过考虑时间和频率因素，在多级决策融合过程中获得最终的癫痫发作检测结果。该模型在两个长期和一个短期公共 EEG 数据库中得到验证。该模型在历元级评估方面的[公式：见正文]-平均值为 97.14%，事件级灵敏度为 96.67%，在长期颅内数据库上的误检率为 0.86/h。在长期头皮数据库中，产生了 95.28% 的纪元级 [公式：见正文] 平均值和 0.81/h 的事件级误检率。