This paper demonstrates the predictive superiority of discrete wavelet transform (DWT) over previously used methods of feature extraction in the diagnosis of epileptic seizures from EEG data. Classification accuracy, specificity, and sensitivity are used as evaluation metrics. We specifically show the immense potential of 2 combinations (DWT-db4 combined with SVM and DWT-db2 combined with RF) as compared to others when it comes to diagnosing epileptic seizures either in the balanced or the imbalanced dataset. The results also highlight that MFCC performs less than all the DWT used in this study and that, The mean-differences are statistically significant respectively in the imbalanced and balanced dataset. Finally, either in the balanced or the imbalanced dataset, the feature extraction techniques, the models, and the interaction between them have a statistically significant effect on the classification accuracy.

中文翻译：

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离散小波变换特征在脑电数据预测癫痫发作中的新用途

本文证明了离散小波变换（DWT）在以前使用的特征提取方法从脑电图数据诊断癫痫发作中的预测优越性。分类准确性，特异性和敏感性用作评估指标。当在平衡或不平衡数据集中诊断癫痫发作时，我们特别显示了两种组合（DWT-db4与SVM组合和DWT-db2与RF组合）的巨大潜力。结果还突出表明，MFCC的性能低于本研究中使用的所有DWT，并且在不平衡和平衡数据集中，均值差异分别具有统计学意义。最后，在平衡或不平衡数据集中，特征提取技术，模型，