Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.
Int J Neural Syst. 2013 Jun;23(3):1350009. doi: 10.1142/S0129065713500093. Epub 2013 Apr 25.
Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.
癫痫是一种慢性脑部疾病，表现为反复发作。通常分析脑电图 (EEG) 信号以研究癫痫发作的特征。在这项工作中，我们提出了一种使用连续小波变换 (CWT)、高阶谱 (HOS) 和纹理对 EEG 信号进行自动分类为正常、发作间期和发作期的方法。首先为 EEG 信号获取 CWT 图，然后从这些图中提取 HOS 和纹理特征。然后将具有统计学意义的特征输入到四个分类器中，即决策树 (DT)、K-最近邻 (KNN)、概率神经网络 (PNN) 和支持向量机 (SVM)，以选择最佳分类器。我们观察到，具有径向基函数 (RBF) 核函数的 SVM 分类器在 EEG 数据的 23.6 秒持续时间内产生了最佳结果，平均准确率为 96%，平均灵敏度为 96.9%，平均特异性为 97%。我们提出的技术可作为自动癫痫监测软件。它还可以帮助医生检查他们开的药物的疗效。
