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Automatic Seizure Detection Based on a Novel Multi-feature Fusion Method and EMD

机译：基于新型多特征融合方法和EMD的癫痫发作自动检测

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摘要

The analysis of electroencephalogram (EEG) signals is becoming more important because of the time-consuming and large bias of traditional visual detection technology, especially in diagnosis of epilepsy. Based on the nonlinear and non-stationary of the EEGs, empirical mode decomposition (EMD) is applied to decompose the original signals into intrinsic mode functions (IMFs). In this paper, after getting the IMFs, the fluctuation index and variation coefficient are calculated to analyze the amplitude change of IMFs. In order to better reflect the information of EEG signals, Hilbert transform is applied to obtain the instantaneous frequency for each IMF. Then, the novel feature named fluctuation index and variation coefficient of instantaneous frequency for IMFs are calculated. Furthermore, feature based on sample entropy of the first order difference is extracted. Finally, both of the calculated features will put together as a fusion feature into SVM for classification. The proposed method is evaluated using the Boon epileptic dataset and the highest average classification accuracy is 99.59%, showing a powerful method to detect seizure.
机译：脑电图（EEG）信号的分析变得越来越重要，因为传统的视觉检测技术非常耗时且偏差很大，尤其是在癫痫的诊断中。基于脑电图的非线性和非平稳性，应用经验模式分解（EMD）将原始信号分解为固有模式函数（IMF）。本文在获得IMF后，通过计算波动指数和变异系数来分析IMF的幅度变化。为了更好地反映脑电信号信息，应用希尔伯特变换获得每个IMF的瞬时频率。然后，计算了IMF的新特征，即波动指数和瞬时频率变化系数。此外，提取基于一阶差的样本熵的特征。最后，两个计算出的特征将作为融合特征放到SVM中进行分类。使用Boon癫痫数据集对提出的方法进行了评估，平均分类的最高准确度为99.59％，显示了检测癫痫发作的强大方法。

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