Epilepsy Seizure Detection in EEG Signals Using Wavelet Transforms and Neural Networks
Epilepsy Seizure Detection in EEG Signals Using Wavelet Transforms and Neural Networks
复制标题
使用小波变换和神经网络检测脑电图信号中的癫痫发作
DOI：
10.1007/978-3-319-06764-3_33
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
P. Gómez
中科院分区：
文献类型：
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作者：
E. Juárez;V. Alarcón;P. Gómez
脑电图（EEG）是脑细胞协同动作产生的电信号的记录，即它们同步动作产生的细胞外场电位的时间过程。脑电图广泛应用于医学领域，用于多种病症的诊断和分析。在本文中，我们提出了一个基于神经网络和小波分析的系统，能够使用脑电图作为输入来识别癫痫发作。这项工作是一项研究的一部分，旨在寻找能够获得比最先进的分类率更好的新模型，以使用脑电图识别正常和癫痫患者。在这里，我们使用离散小波变换 (DWT) 和最大重叠离散小波变换 (MODWT) 进行特征提取，并使用前馈人工神经网络 (FF-ANN) 进行分类。通过使用波恩大学提供的基准数据库，我们的方法通过三重交叉验证获得了 99.26% 的平均准确率，这比使用类似策略的其他工作要好。
An electroencephalogram (EEG) is a record of the electric signal generated by the cooperative action of brain cells, that is, the time course of extracellular field potentials generated by their synchronous action. EEG is widely used in medicine for diagnostic and analysis of several conditions. In this paper, we present a system based on neural networks and wavelet analysis, able to identify epilepsy seizures using EEG as inputs. This work is part of a research looking for novel models able to obtain classification rates better that the state-of-the-art, for the identification of normal and epileptic patients using EEG. Here we present results using a Discrete Wavelet Transform (DWT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) for feature extraction and Feed-Forward Artificial Neural Networks (FF-ANN) for classification. By using the benchmark database provided by the University of Bonn, our approach obtains an average accuracy of 99.26 % tested using threefold cross-validation, which is better than other works using similar strategies.