Pang Clement C C, Upton Adrian R M, Shine Glenn, Kamath Markad V
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8N 3Z5, Canada.
IEEE Trans Biomed Eng. 2003 Apr;50(4):521-6. doi: 10.1109/TBME.2003.809479.
Identification of the short transient waveform, called a spike, in the cortical electroencephalogram (EEG) plays an important role during diagnosis of neurological disorders such as epilepsy. It has been suggested that artificial neural networks (ANN) can be employed for spike detection in the EEG, if suitable features are provided as input to an ANN. In this paper, we explore the performance of neural network-based classifiers using features selected by algorithms suggested by four previous investigators. Of these, three algorithms model the spike by mathematical parameters and use them as features for classification while the fourth algorithm uses raw EEG to train the classifier. The objective of this paper is to examine if there is any inherent advantage to any particular set of features, subject to the condition that the same data are used for all feature selection algorithms. Our results suggest that artificial neural networks trained with features selected using any one of the above three algorithms as well as raw EEG directly fed to the ANN will yield similar results.
在皮质脑电图（EEG）中识别被称为尖峰的短暂瞬态波形，在癫痫等神经系统疾病的诊断中起着重要作用。有人提出，如果将合适的特征作为人工神经网络（ANN）的输入，那么可以将其用于EEG中的尖峰检测。在本文中，我们使用由之前四位研究者提出的算法所选择的特征，来探索基于神经网络的分类器的性能。其中，三种算法通过数学参数对尖峰进行建模，并将其用作分类特征，而第四种算法使用原始EEG来训练分类器。本文的目的是在所有特征选择算法都使用相同数据的条件下，检验是否存在任何一组特定特征具有内在优势。我们的结果表明，使用上述三种算法中的任何一种选择的特征进行训练的人工神经网络，以及直接将原始EEG输入到ANN中，都会产生相似的结果。
