论文标题
基于多纸光谱特征的二进制和多类分类器用于癫痫检测
Binary and Multiclass Classifiers based on Multitaper Spectral Features for Epilepsy Detection
论文作者
论文摘要
癫痫是可以通过脑电图(EEG)诊断出的最常见的神经系统疾病之一,可以观察到以下癫痫事件:前,剧院,疾病,疾病,后,剧院和临时。在本文中,我们提出了一种新的癫痫检测方法,分为两个分化环境:二进制和多类分类。为了提取特征,从功率谱,光谱图和双光谱图中总共提取了105次措施。对于分类器构建,使用了八种不同的机器学习算法。我们的方法应用于广泛使用的EEG数据库中。结果,基于多层感知算法的随机森林和反向传播分别达到了二进制(98.75%)和多类(96.25%)分类问题的最高精度。随后,统计测试找不到比其他分类器更好的性能的模型。在基于混淆矩阵的评估中,也无法识别与其他脑电图分类模型相关的分类器。即便如此,我们的结果还是在文献中的发现也很有希望和竞争。
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.