Background Brain signals (EEG—Electroencephalography) are a gold standard frequently used in epilepsy prediction. It is crucial to predict epilepsy, which is common in the community. Early diagnosis is essential to reduce the treatment process of the disease and to keep the process healthier. Methods In this study, a five-classes dataset was used: EEG signals from different individuals, healthy EEG signals from tumor document, EEG signal with epilepsy, EEG signal with eyes closed, and EEG signal with eyes open. Four different methods have been proposed to classify five classes of EEG signals. In the first approach, the EEG signal was first divided into four different bands (beta, alpha, theta, and delta), and then 25 time-domain features were extracted from each band, and the main EEG signal and these extracted features were combined to obtain 125-time domain features (feature extraction). Using the Random Forests classifier, EEG activities were classified into five classes. In the second approach, each One-Against-One (OVO) approach with 125 attributes was split into ten parts, pairwise, and then each piece was classified with the Random Forests classifier. The majority voting scheme was used to combine decisions from the ten classifiers. In the third proposed method, each One-Against-All (OVA) approach with 125 attributes was divided into five parts, and then each piece was classified with the Random Forests classifier. The majority voting scheme was used to combine decisions from the five classifiers. In the fourth proposed approach, each One-Against-All (OVA) approach with 125 attributes was divided into five parts. Since each piece obtained had an imbalanced data distribution, an adaptive synthetic (ADASYN) sampling approach was used to stabilize each piece. Then, each balanced piece was classified with the Random Forests classifier. To combine the decisions obtanied from each classifier, the majority voting scheme has been used. Results The first approach achieved 71.90% classification success in classifying five-class EEG signals. The second approach achieved a classification success of 91.08% in classifying five-class EEG signals. The third method achieved 89% success, while the fourth proposed approach achieved 91.72% success. The results obtained show that the proposed fourth approach (the combination of the ADASYN sampling approach and Random Forest Classifier) achieved the best success in classifying five class EEG signals. This proposed method could be used in the detection of epilepsy events in the EEG signals.

中文翻译：

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基于自适应合成采样（ADASYN）方法的新型多类不平衡脑电信号分类

背景 脑信号（EEG-脑电图）是癫痫预测中经常使用的黄金标准。预测癫痫症至关重要，这在社区中很常见。早期诊断对于减少疾病的治疗过程并保持过程更健康至关重要。方法 在本研究中，使用五类数据集：来自不同个体的脑电信号、来自肿瘤文件的健康脑电信号、癫痫脑电信号、闭眼脑电信号和睁眼脑电信号。已经提出了四种不同的方法来对五类 EEG 信号进行分类。在第一种方法中，首先将脑电信号分为四个不同的波段（beta、alpha、theta 和 delta），然后从每个波段中提取 25 个时域特征，并将主脑电信号和这些提取的特征结合起来，得到125个时域的特征（特征提取）。使用随机森林分类器，脑电图活动分为五类。在第二种方法中，每个具有 125 个属性的一对一（OVO）方法被分成十部分，成对的，然后用随机森林分类器对每一部分进行分类。多数投票方案用于组合来自十个分类器的决策。在第三种方法中，每个具有 125 个属性的 One-Against-All (OVA) 方法被分为五个部分，然后用随机森林分类器对每个部分进行分类。多数投票方案用于组合来自五个分类器的决策。在第四种建议的方法中，每个具有 125 个属性的 One-Against-All (OVA) 方法分为五个部分。由于获得的每个片段都有不平衡的数据分布，因此使用自适应合成 (ADASYN) 采样方法来稳定每个片段。然后，使用随机森林分类器对每个平衡块进行分类。为了结合从每个分类器中获得的决策，使用了多数投票方案。结果 第一种方法在对五类脑电信号进行分类时取得了 71.90% 的分类成功率。第二种方法在对五类脑电信号进行分类时取得了 91.08% 的分类成功率。第三种方法取得了 89% 的成功率，而提出的第四种方法取得了 91.72% 的成功率。获得的结果表明，所提出的第四种方法（ADASYN 采样方法和随机森林分类器的组合）在对五类 EEG 信号进行分类方面取得了最佳成功。这种提出的方​​法可用于检测 EEG 信号中的癫痫事件。