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首页> 外文期刊>Biomedical Engineering: Applications, Basis and Communications >CLASSIFICATION OF ICTAL EEG USING MODELING BASED SPECTRAL AND TEMPORAL FEATURES ON INSTANTANEOUS AMPLITUDE-FREQUENCY COMPONENTS OF IMFs
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CLASSIFICATION OF ICTAL EEG USING MODELING BASED SPECTRAL AND TEMPORAL FEATURES ON INSTANTANEOUS AMPLITUDE-FREQUENCY COMPONENTS OF IMFs

机译：基于模拟的基于模拟的ICFS速度幅度分量分类ICTAL EEG的分类

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摘要

In the present study, a method for classifying the different ictal stages in electroencephalogram (EEG) signals is proposed. The main symptoms of epilepsy are indicated by ictal activities, which trigger widespread neurological disorders other than stroke and thus affect the world population. In this work, a novel ictal classification method that combines the spectral and temporal features of twin components in Hilbert–Huang transform is proposed. Spectral features of instantaneous amplitude (IA) function are obtained based on the power spectral density of autoregressive (AR) modeling. Here four different cases of ictal activities of EEG signal are classified. In each case first and second intrinsic mode function of Hilbert–Huang transform are tabulated. The power spectral density of AR(6) and AR(10) model are done for IA1 and IA2 components of each case. Temporal features of either instantaneous frequency (IF) function or IA are computed. The feature vectors are tested in a well-known database of different classes in interictal, ictal, and normal activities of EEG signals. The discriminating power of each vector is evaluated through one-way analysis of variance, and the classification results are verified using an artificial neural network (ANN) classifier. The performance of the classifier was assessed in term of sensitivity, specificity, and total classification accuracy. The spectral features of the AR(10) of IA and the temporal features of IA yielded 100% accuracy, 100% sensitivity, and 100% specificity in the ictal classification. By contrast, these features obtained only 83.33% of the total classification accuracy in ictal and interictal EEG signal.
机译：在本研究中，提出了一种用于对脑电图（EEG）信号中的不同ICTAL阶段进行分类的方法。癫痫的主要症状由ICTAL活动表明，这引发了除卒中以外的广泛神经系统障碍，从而影响世界人口。在这项工作中，提出了一种结合Hilbert-Huang变换中双组分的光谱和时间特征的新型ICTAL分类方法。基于自回归（AR）建模的功率谱密度获得瞬时幅度（IA）功能的光谱特征。这里归类于脑电图信号的四种不同案例活动。在每种情况下，Hilbert-Huang变换的第一和第二内在模式功能都是制表的。 AR（6）和Ar（10）模型的功率谱密度是针对每种情况的IA1和IA2组分进行的。计算瞬时频率（IF）函数或IA的时间特征。特征向量在eEG信号的互联网，ictal和正常活动中的不同类别的众所周知的数据库中进行测试。通过单向的方差分析来评估每个矢量的区分功率，并且使用人工神经网络（ANN）分类器来验证分类结果。分类器的性能是在灵敏度，特异性和总分类准确性的术语期间进行评估。 IA的AR（10）的光谱特征和Ia的时间特征在ICTAL分类中产生100％的精度，100％敏感性和100％的特异性。相比之下，这些功能仅在ICTAL和Intertical EEG信号中获得了总分类准确性的83.33％。

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