Subasi Abdulhamit, Erçelebi Ergun
Department of Electrical and Electronics Engineering, Kahramanmaras Sutcu Imam University, 46601 Kahramanmaraş, Turkey.
Comput Methods Programs Biomed. 2005 May;78(2):87-99. doi: 10.1016/j.cmpb.2004.10.009.
Epileptic seizures are manifestations of epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper deals with a novel method of analysis of EEG signals using wavelet transform and classification using artificial neural network (ANN) and logistic regression (LR). Wavelet transform is particularly effective for representing various aspects of non-stationary signals such as trends, discontinuities and repeated patterns where other signal processing approaches fail or are not as effective. Through wavelet decomposition of the EEG records, transient features are accurately captured and localized in both time and frequency context. In epileptic seizure classification we used lifting-based discrete wavelet transform (LBDWT) as a preprocessing method to increase the computational speed. The proposed algorithm reduces the computational load of those algorithms that were based on classical wavelet transform (CWT). In this study, we introduce two fundamentally different approaches for designing classification models (classifiers) the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on ANN. Logistic regression as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. In these methods we used LBDWT coefficients of EEG signals as an input to classification system with two discrete outputs: epileptic seizure or non-epileptic seizure. By identifying features in the signal we want to provide an automatic system that will support a physician in the diagnosing process. By applying LBDWT in connection with MLPNN, we obtained novel and reliable classifier architecture. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.
癫痫发作是癫痫的表现形式。对脑电图（EEG）记录进行仔细分析，可为深入了解导致癫痫疾病的机制提供有价值的见解并增进认识。脑电图中癫痫样放电的检测是癫痫诊断的重要组成部分。由于脑电信号是非平稳的，传统的频率分析方法在诊断分类中效果不佳。本文探讨了一种使用小波变换分析脑电信号的新方法，并利用人工神经网络（ANN）和逻辑回归（LR）进行分类。小波变换在表示非平稳信号的各个方面（如趋势、不连续性和重复模式）时特别有效，而其他信号处理方法在此类情况中效果不佳或不太有效。通过对脑电图记录进行小波分解，可以在时间和频率范围内准确捕获并定位瞬态特征。在癫痫发作分类中，我们使用基于提升的离散小波变换（LBDWT）作为预处理方法来提高计算速度。所提出的算法降低了基于经典小波变换（CWT）的算法的计算量。在本研究中，我们介绍了两种设计分类模型（分类器）的根本不同的方法：基于逻辑回归的传统统计方法和基于人工神经网络的新兴强大计算技术。开发了基于逻辑回归以及基于多层感知器神经网络（MLPNN）的分类器，并就它们在脑电信号分类中的准确性进行了比较。在这些方法中，我们将脑电信号的LBDWT系数用作分类系统的输入，该系统有两个离散输出：癫痫发作或非癫痫发作。通过识别信号中的特征，我们希望提供一个自动系统，以在诊断过程中为医生提供支持。通过将LBDWT与MLPNN结合应用，我们获得了新颖且可靠的分类器架构。所开发的分类器之间的比较主要基于对接收器操作特征（ROC）曲线的分析以及与分类相关的一些标量性能指标。基于MLPNN的分类器优于基于LR的分类器。在同一组中，基于MLPNN的分类器比基于LR的分类器更准确。
