Patnaik L M, Manyam Ohil K
Computational Neurobiology Group, Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560012, India.
Comput Methods Programs Biomed. 2008 Aug;91(2):100-9. doi: 10.1016/j.cmpb.2008.02.005. Epub 2008 Apr 14.
Electroencephalogram (EEG) has established itself as an important means of identifying and analyzing epileptic seizure activity in humans. In most cases, identification of the epileptic EEG signal is done manually by skilled professionals, who are small in number. In this paper, we try to automate the detection process. We use wavelet transform for feature extraction and obtain statistical parameters from the decomposed wavelet coefficients. A feed-forward backpropagating artificial neural network (ANN) is used for the classification. We use genetic algorithm for choosing the training set and also implement a post-classification stage using harmonic weights to increase the accuracy. Average specificity of 99.19%, sensitivity of 91.29% and selectivity of 91.14% are obtained.
脑电图（EEG）已成为识别和分析人类癫痫发作活动的重要手段。在大多数情况下，癫痫脑电信号的识别是由少数技术熟练的专业人员手动完成的。在本文中，我们试图实现检测过程的自动化。我们使用小波变换进行特征提取，并从分解后的小波系数中获得统计参数。使用前馈反向传播人工神经网络（ANN）进行分类。我们使用遗传算法选择训练集，并使用谐波权重实现分类后阶段以提高准确率。获得了99.19%的平均特异性、91.29%的灵敏度和91.14%的选择性。
