Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection
Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection
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DOI：
10.1109/tbme.2007.905490
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发表时间：
2008-02-01
影响因子：
4.6
通讯作者：
Dadmehr, Nahid
中科院分区：
文献类型：
--
作者：
Ghosh-Dastidar, Samanwoy;Adeli, Hojat;Dadmehr, Nahid
提出了一种新的主成分分析增强余弦径向基函数神经网络分类器。该两阶段分类器与作者早期开发的混合波段小波混沌方法相结合，可以准确而稳健地将脑电图（eeg）分类为健康、发作和间歇期脑电图。使用先前研究中发现的用于有效脑电表示的九参数混合频带特征空间作为两阶段分类器的输入。第一阶段，采用PCA进行特征增强。的。输入空间沿数据主成分的重排显著提高了余弦径向基函数神经网络（RBFNN）在第二阶段的分类精度。通过大量的参数分析和灵敏度分析，验证了分类器的分类精度和鲁棒性。新的小波混沌神经网络方法具有较高的脑电分类准确率（96.6%），并且对训练数据的变化具有较低的鲁棒性，标准差为1.4%。对于癫痫的诊断，当只考虑正常脑电图和间歇脑电图时，该模型的分类准确率为99.3%。这一统计数据尤其引人注目，因为即使是最训练有素的神经科医生似乎也无法在80%以上的情况下检测到间歇脑电图。
A novel principal component analysis (PCA)-enhanced cosine radial basis function neural network classifier is presented. The two-stage classifier is integrated with the mixed-band wavelet-chaos methodology, developed earlier by the authors, for accurate and robust classification of electroencephalogram (EEGs) into healthy, ictal, and interictal EEGs. A nine-parameter mixed-band feature space discovered in previous research for effective EEG representation is used as input to the two-stage classifier. In the first stage, PCA is employed for feature enhancement. The. rearrangement of the input space along the principal components of the data improves the classification accuracy of the cosine radial basis function neural network (RBFNN) employed in the second stage significantly. The classification accuracy and robustness of the classifier are validated by extensive parametric and sensitivity analysis. The new wavelet-chaos-neural network methodology yields high EEG classification accuracy (96.6%) and is quite robust to changes in training data with a low standard deviation of 1.4%. For epilepsy diagnosis, when only normal and interictal EEGs are considered, the classification accuracy of the proposed model is 99.3%. This statistic is especially remarkable because even the most highly trained neurologists do not appear to be able to detect interictal EEGs more than 80% of the times.