Moctezuma Luis Alfredo, Molinas Marta
Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim 7434, Norway.
J Biomed Res. 2019 Aug 29;34(3):180-190. doi: 10.7555/JBR.33.20190009.
We are here to present a new method for the classification of epileptic seizures from electroencephalogram (EEG) signals. It consists of applying empirical mode decomposition (EMD) to extract the most relevant intrinsic mode functions (IMFs) and subsequent computation of the Teager and instantaneous energy, Higuchi and Petrosian fractal dimension, and detrended fluctuation analysis (DFA) for each IMF. We validated the method using a public dataset of 24 subjects with EEG signals from 22 channels and showed that it is possible to classify the epileptic seizures, even with segments of six seconds and a smaller number of channels ( ., an accuracy of 0.93 using five channels). We were able to create a general machine-learning-based model to detect epileptic seizures of new subjects using epileptic-seizure data from various subjects, after reducing the number of instances, based on the k-means algorithm.
我们在此介绍一种从脑电图（EEG）信号中对癫痫发作进行分类的新方法。该方法包括应用经验模态分解（EMD）来提取最相关的本征模态函数（IMF），随后对每个IMF计算Teager和瞬时能量、Higuchi和Petrosian分形维数以及去趋势波动分析（DFA）。我们使用一个包含24名受试者、来自22个通道的EEG信号的公共数据集对该方法进行了验证，结果表明即使使用六秒的片段和较少数量的通道（例如，使用五个通道时准确率为0.93），也能够对癫痫发作进行分类。在基于k均值算法减少实例数量后，我们能够创建一个基于机器学习的通用模型，使用来自不同受试者的癫痫发作数据来检测新受试者的癫痫发作。
