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首页> 外文会议>Proceedings of 2010 International Conference on Systems in Medicine and Biology >Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction
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Classification of EEG signal using wavelet transform and support vector machine for epileptic seizure diction

机译：基于小波变换和支持向量机的脑电信号癫痫发作分类

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Feature extraction and classification of electroencephalogram (EEGs) signals for (normal and epileptic) is a challenge for engineers and scientists. Various signal processing techniques have already been proposed for classification of non-linear and non- stationary signals like EEG. In this work, SVM (support vector machine) based classifier was employed to detect epileptic seizure activity from background electro encephalographs (EEGs). Five types of EEG signals (healthy subject with eye open condition, eye close condition, epileptic, seizure signal from hippocampal region) were selected for the analysis. Signals were preprocessed, decomposed by using discrete wavelet transform DWT till 5th level of decomposition tree. Various features like energy, entropy and standard deviation were computed and consequently used for classification of signals. The results show the promising classification accuracy of nearly 91.2% in detection of abnormal from normal EEG signals. This proposed classifier can be used to design expert system for epilepsy diagnosis purpose in various hospitals.
机译：对（正常和癫痫性）脑电图（EEG）信号进行特征提取和分类对工程师和科学家来说是一个挑战。已经提出了各种信号处理技术来对诸如EEG之类的非线性和非平稳信号进行分类。在这项工作中，基于支持向量机（SVM）的分类器被用于从背景脑电图仪（EEG）中检测癫痫发作的活动。选择五种EEG信号（健康受试者的眼张开，闭眼，癫痫，海马区癫痫发作信号）进行分析。信号经过预处理，使用离散小波变换DWT进行分解，直到分解树达到第5级。计算了诸如能量，熵和标准偏差之类的各种特征，因此将其用于信号分类。结果表明，在检测正常脑电信号异常中，有希望的分类准确率接近91.2％。所提出的分类器可用于设计各种医院中用于癫痫诊断目的的专家系统。

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