Kiymik M Kemal, Subasi Abdulhamit, Ozcalik H Riza
Department of Electrical and Electronics Engineering, Kahramanmaraş Sütçü Imam University, 46100 Kahramanmaras, Turkey.
J Med Syst. 2004 Dec;28(6):511-22. doi: 10.1023/b:joms.0000044954.85566.a9.
Approximately 1% of the people in the world suffer from epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. Predicting the onset of epileptic seizure is an important and difficult biomedical problem, which has attracted substantial attention of the intelligent computing community over the past two decades. The purpose of this work was to investigate the performance of the periodogram and autoregressive (AR) power spectrum methods to extract classifiable features from human electroencephalogram (EEG) by using artificial neural networks (ANN). The feedforward ANN system was trained and tested with the backpropagation algorithm using a large data set of exemplars. We present a method for the automatic comparison of epileptic seizures in EEG, allowing the grouping of seizures having similar overall patterns. Each channel of the EEG is first broken down into segments having relatively stationary characteristics. Features are then calculated for each segment, and all segments of all channels of the seizures of a patient are grouped into clusters of similar morphology. This clustering allows labeling of every EEG segment. Examples from 5 patients with scalp electrodes illustrate the ability of the method to group seizures of similar morphology. It was observed that ANN classification of EEG signals with AR preprocessing gives better results, and these results can also be used for the deduction of epileptic seizure.
世界上约1%的人患有癫痫。对脑电图（EEG）记录进行仔细分析，可为深入了解癫痫疾病的发病机制提供有价值的见解。预测癫痫发作的开始是一个重要且困难的生物医学问题，在过去二十年中引起了智能计算领域的广泛关注。这项工作的目的是研究通过使用人工神经网络（ANN），利用周期图和自回归（AR）功率谱方法从人类脑电图（EEG）中提取可分类特征的性能。使用大量示例数据集，通过反向传播算法对前馈ANN系统进行训练和测试。我们提出了一种自动比较脑电图中癫痫发作的方法，可对具有相似整体模式的发作进行分组。首先将脑电图的每个通道分解为具有相对稳定特征的片段。然后为每个片段计算特征，并将患者癫痫发作的所有通道的所有片段分组为形态相似的簇。这种聚类允许对每个脑电图片段进行标记。来自5名头皮电极患者的示例说明了该方法对相似形态癫痫发作进行分组的能力。研究发现，经过AR预处理的脑电图信号的ANN分类能给出更好的结果，这些结果也可用于癫痫发作的推断。
