基于多锥度谱特征的二值和多类分类器在癫痫检测中的应用
Binary and Multiclass Classifiers based on Multitaper Spectral Features for Epilepsy Detection
摘要：
癫痫是最常见的神经系统疾病之一，可以通过脑电图（EEG）进行诊断，其中可以观察到以下癫痫事件：发作前、发作、发作后和发作间期。在本文中，我们提出了一种新的方法，癫痫检测到两个分化的背景下：二进制和多类分类。对于特征提取，从功率谱、频谱图和双频谱图中提取总共105个测量值。对于分类器构建，使用了八种不同的机器学习算法。我们的方法被应用在一个广泛使用的EEG数据库。因此，随机森林和反向传播的多层感知器算法的基础上达到最高的精度为二进制（98.75%）和多类（96.25%）分类问题，分别。随后，统计测试没有找到一个模型，将实现比其他分类器更好的性能。在基于混淆矩阵的评估中，也不可能识别出与用于EEG分类的其他模型相比突出的分类器。即便如此，我们的结果是有希望的，并与文献中的发现竞争。
英文摘要：
Epilepsy is one of the most common neurological disorders that can be diagnosed through electroencephalogram (EEG), in which the following epileptic events can be observed: pre-ictal, ictal, post-ictal, and interictal. In this paper, we present a novel method for epilepsy detection into two differentiation contexts: binary and multiclass classification. For feature extraction, a total of 105 measures were extracted from power spectrum, spectrogram, and bispectrogram. For classifier building, eight different machine learning algorithms were used. Our method was applied in a widely used EEG database. As a result, random forest and backpropagation based on multilayer perceptron algorithms reached the highest accuracy for binary (98.75%) and multiclass (96.25%) classification problems, respectively. Subsequently, the statistical tests did not find a model that would achieve a better performance than the other classifiers. In the evaluation based on confusion matrices, it was also not possible to identify a classifier that stands out in relation to other models for EEG classification. Even so, our results are promising and competitive with the findings in the literature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
