Data_Sheet_1_EEG Channel-Selection Method for Epileptic-Seizure Classification Based on Multi-Objective Optimization.PDF

We present a multi-objective optimization method for electroencephalographic (EEG) channel selection based on the non-dominated sorting genetic algorithm (NSGA) for epileptic-seizure classification. We tested the method on EEG data of 24 patients from the CHB-MIT public dataset. The procedure starts by decomposing the EEG data from each channel into different frequency bands using the empirical mode decomposition (EMD) or the discrete wavelet transform (DWT), and then for each sub-band four features are extracted; two energy values and two fractal dimension values. The obtained feature vectors are then iteratively tested for solving two unconstrained objectives by NSGA-II or NSGA-III; to maximize classification accuracy and to reduce the number of EEG channels required for epileptic seizure classification. Our results have shown accuracies of up to 1.00 with only one EEG channel. Interestingly, when using all the EEG channels available, lower accuracies were achieved compared to the case when EEG channels were selected by NSGA-II or NSGA-III; i.e., in patient 19 we obtained an accuracy of 0.95 using all the channels and 0.975 using only two channels selected by NSGA-III. The results obtained are encouraging and it has been shown that it is possible to classify epileptic seizures using a few electrodes, which provide evidence for the future development of portable EEG seizure detection devices.

本研究提出了一种基于非支配排序遗传算法（NSGA）的多目标优化方法，旨在对癫痫发作进行脑电图（EEG）通道选择。该方法在CHB-MIT公共数据集中24位患者的EEG数据上进行了测试。首先，通过对每个通道的EEG数据进行经验模态分解（EMD）或离散小波变换（DWT），将其分解为不同的频段，然后对每个子带提取四个特征；包括两个能量值和两个分形维数值。随后，通过NSGA-II或NSGA-III算法对所获得的特征向量进行迭代测试，以解决两个无约束目标；即最大化分类准确率，并减少用于癫痫发作分类所需的EEG通道数量。实验结果表明，仅使用一个EEG通道即可达到高达1.00的准确率。有趣的是，当使用所有可用的EEG通道时，与通过NSGA-II或NSGA-III选择通道的情况相比，所获得的准确率较低；例如，在患者19中，使用所有通道时准确率为0.95，而使用NSGA-III选择的两通道时准确率则达到0.975。所取得的结果令人鼓舞，并已证实使用少数电极即可对癫痫发作进行分类，这为便携式EEG癫痫发作检测设备的发展提供了有力证据。