Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489.
Int J Neural Syst. 2012 Apr;22(2):1250002. doi: 10.1142/S0129065712500025.
Epilepsy, a neurological disorder, is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals, which are used to detect the presence of seizures, are non-linear and dynamic in nature. Visual inspection of the EEG signals for detection of normal, interictal, and ictal activities is a strenuous and time-consuming task due to the huge volumes of EEG segments that have to be studied. Therefore, non-linear methods are being widely used to study EEG signals for the automatic monitoring of epileptic activities. The aim of our work is to develop a Computer Aided Diagnostic (CAD) technique with minimal pre-processing steps that can classify all the three classes of EEG segments, namely normal, interictal, and ictal, using a small number of highly discriminating non-linear features in simple classifiers. To evaluate the technique, segments of normal, interictal, and ictal EEG segments (100 segments in each class) were used. Non-linear features based on the Higher Order Spectra (HOS), two entropies, namely the Approximation Entropy (ApEn) and the Sample Entropy (SampEn), and Fractal Dimension and Hurst Exponent were extracted from the segments. Significant features were selected using the ANOVA test. After evaluating the performance of six classifiers (Decision Tree, Fuzzy Sugeno Classifier, Gaussian Mixture Model, K-Nearest Neighbor, Support Vector Machine, and Radial Basis Probabilistic Neural Network) using a combination of the selected features, we found that using a set of all the selected six features in the Fuzzy classifier resulted in 99.7% classification accuracy. We have demonstrated that our technique is capable of achieving high accuracy using a small number of features that accurately capture the subtle differences in the three different types of EEG (normal, interictal, and ictal) segments. The technique can be easily written as a software application and used by medical professionals without any extensive training and cost. Such software can evolve into an automatic seizure monitoring application in the near future and can aid the doctors in providing better and timely care for the patients suffering from epilepsy.
癫痫是一种神经系统疾病，其特征是反复发作。脑电图 (EEG) 信号用于检测癫痫发作，本质上是非线性和动态的。由于必须研究大量的 EEG 段，因此通过视觉检查 EEG 信号来检测正常、发作间期和发作期活动是一项艰巨且耗时的任务。因此，人们广泛使用非线性方法来研究 EEG 信号，以实现对癫痫活动的自动监测。我们的工作旨在开发一种计算机辅助诊断 (CAD) 技术，该技术具有最小的预处理步骤，使用少数高度有区别的非线性特征和简单的分类器即可对所有三种 EEG 段（正常、发作间期和发作期）进行分类。为了评估该技术，使用了正常、发作间期和发作期 EEG 段（每类 100 个段）。从段中提取了基于高阶谱 (HOS) 的非线性特征、两种熵，即近似熵 (ApEn) 和样本熵 (SampEn) 以及分形维数和赫斯特指数。使用 ANOVA 检验选择了显著特征。在使用所选特征组合评估了六种分类器（决策树、模糊 Sugeno 分类器、高斯混合模型、K-最近邻、支持向量机和径向基概率神经网络）的性能后，我们发现使用模糊分类器中的所有六组选定特征集可达到 99.7%的分类准确性。我们已经证明，我们的技术能够使用少量准确捕获三种不同类型 EEG（正常、发作间期和发作期）段之间细微差异的特征来实现高精度。该技术可以很容易地编写为软件应用程序，并由医疗专业人员使用，而无需进行任何广泛的培训和成本。在不久的将来，此类软件可以演变为自动癫痫监测应用程序，并可以帮助医生为癫痫患者提供更好和及时的护理。
