BACKGROUND Epilepsy is one of the most common neurological disorders associated with disruption of brain activity. In the classification and detection of epileptic seizures, electroencephalography (EEG) measurements, which record the electrical activities of the brain, are frequently used. Empirical mode decomposition (EMD) and its derivative, ensemble EMD (EEMD) are recently developed methods used to decompose non-stationary and nonlinear signals such as EEG into a finite number of oscillations called intrinsic mode functions (IMFs). Our main objective in this study is to present a hybrid IMF selection method combining four different approaches (energy, correlation, power spectral distance, and statistical significance measures), and investigate the effect of selected IMFs extracted by EMD and EEMD on the classification. We have applied the proposed IMF selection approach on the classification of EEG signals recorded from epilepsy patients who are under treatment at our collaborator hospital. Multichannel EEG signals collected from epilepsy patients are decomposed into IMFs, and then IMF selection was performed. Finally, time- and spectral-domain, and nonlinear features are extracted and feature sets are created for the classification. RESULTS The maximum classification accuracies obtained using various combinations of IMFs were 94.56%, 95.63%, 96.8%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression classifiers, respectively, by using EMD analysis; whereas, the EEMD approach has provided maximum classification accuracies of 96.06%, 97%, 97%, and 96.25% for SVM, KNN, naive Bayes, and logistic regression, respectively. Classification performance with the same features obtained using direct EEG signals instead of the decomposed IMFs was worse than the aforementioned 2 approaches for every combination. CONCLUSION Simulation results demonstrate that the proposed IMF selection approach affects the classification results. Also, EEMD provides a robust method for feature extraction from EEG signals in order to classify pre-seizure and seizure segments.

中文翻译：

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使用经验模式分解及其派生的癫痫发作分类。

背景技术癫痫是与脑活动中断有关的最常见的神经系统疾病之一。在癫痫发作的分类和检测中，经常使用记录脑电活动的脑电图（EEG）测量。经验模态分解（EMD）及其导数集成EMD（EEMD）是最近开发的方法，用于将非平稳和非线性信号（例如EEG）分解为有限数量的振荡，称为固有模式函数（IMF）。我们这项研究的主要目的是提出一种混合IMF选择方法，该方法结合了四种不同方法（能量，相关性，功率谱距离和统计显着性度量），并研究了EMD和EEMD提取的所选IMF对分类的影响。我们已将建议的IMF选择方法应用于在合作医院接受治疗的癫痫患者记录的EEG信号分类。从癫痫患者中收集的多通道EEG信号分解为IMF，然后进行IMF选择。最后，提取时域和光谱域以及非线性特征，并为分类创建特征集。结果通过EMD分析，使用各种IMF组合获得的SVM，KNN，朴素贝叶斯和逻辑回归分类器的最大分类精度分别为94.56％，95.63％，96.8％和96.25％。EEMD方法为SVM，KNN，朴素贝叶斯和逻辑回归分别提供了96.06％，97％，97％和96.25％的最大分类精度。对于每种组合，使用直接脑电信号代替分解的IMF所获得的具有相同特征的分类性能要比上述两种方法差。结论仿真结果表明，提出的IMF选择方法会影响分类结果。此外，EEMD还提供了一种从脑电信号中提取特征的强大方法，以便对癫痫发作前和发作期进行分类。