Abdulhay Enas, Alafeef Maha, Abdelhay Arwa, Al-Bashir Areen
1Department of Biomedical Engineering, Faculty of Engineering, Jordan University of Science and Technology, P.O.Box 3030, Irbid, 22110 Jordan.
2Department of Water and Environmental Engineering, Faculty of Natural Resources Engineering, German Jordanian University, P.O.Box 35247, Amman, 11180 Jordan.
J Med Biol Eng. 2017;37(6):843-857. doi: 10.1007/s40846-017-0239-z. Epub 2017 Jun 19.
This paper presents an accurate nonlinear classification method that can help physicians diagnose seizure in electroencephalographic (EEG) signal characterized by a disturbance in temporal and spectral content. This is accomplished by applying four steps. First, different EEG signals containing healthy, ictal and seizure-free (inter-ictal) activities are decomposed by empirical mode decomposition method. The instantaneous amplitudes and frequencies of resulted bands (intrinsic mode functions, IMF) are then tracked by the direct quadrature method (DQ). In contrast to other approaches, DQ cancels the effect of amplitude modulation on frequency calculation. The dissociation between instantaneous amplitude and frequency information is therefore fully achieved to avoid features confusion. Afterwards, the Shannon entropy values of both sets of instantaneous values (amplitudes and frequencies)-related to every IMF-are calculated. Finally, the obtained entropy values are classified by random forest tree. The proposed procedure yields 100% accuracy for (healthy)/(ictal) and 98.3-99.7% for (healthy)/(ictal)/(interictal) classification problems. The suggested method is hence robust, accurate, fast, user-friendly, data driven with open access interpretability.
本文提出了一种精确的非线性分类方法，该方法可帮助医生在以时间和频谱内容紊乱为特征的脑电图（EEG）信号中诊断癫痫发作。这通过四个步骤来实现。首先，采用经验模式分解方法对包含健康、发作期和发作间期活动的不同EEG信号进行分解。然后，通过直接正交方法（DQ）跟踪所得频段（本征模函数，IMF）的瞬时幅度和频率。与其他方法相比，DQ消除了幅度调制对频率计算的影响。因此，完全实现了瞬时幅度和频率信息的分离，以避免特征混淆。之后，计算与每个IMF相关的两组瞬时值（幅度和频率）的香农熵值。最后，通过随机森林树对获得的熵值进行分类。对于（健康）/（发作期）分类问题，所提出的过程准确率为100%，对于（健康）/（发作期）/（发作间期）分类问题，准确率为98.3 - 99.7%。因此，所建议的方法具有鲁棒性、准确性、快速性、用户友好性，是数据驱动且具有开放访问可解释性的。
