• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2017 15th International Conference on Quality in Research : International Symposium on Electrical and Computer Engineering >Relative wavelet bispectrum feature for alcoholic EEG signal classification using artificial neural network
【24h】

Relative wavelet bispectrum feature for alcoholic EEG signal classification using artificial neural network

机译：相对小波双谱特征用于酒精性脑电信号的人工神经网络分类

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper proposes a novel relative wavelet bispectrum (RWB) approach for EEG signal feature extraction method to differentiate the signal between the alcoholic over the non-alcoholic subjects. Firstly, the EEG signal is calculated for its autocorrelation frequencies as the basic step in the bispectrum calculation. Then, the discrete wavelet transform (DWT) is applied substituting the FFT which usually is used in the bispectrum calculation. Lastly, the relative value of each frequency band is calculated for both the approximation and the details parts, producing the RWB. The proposed methodology is implemented in an alcoholic automated detection system using 1200 data samples from UCI EEG Database for alcoholism. Based on the experiments, the setting value of lag in the autocorrelation calculation was evidently very influential on the recognition rate obtained, i.e. the maximum value for the lag was the best. Using cross validation, the highest results from RWB feature extraction method with ANN classifier achieved about 90% recognition rate.
机译：本文提出了一种新的相对小波双谱（RWB）方法用于脑电信号特征提取方法，以区分酒精与非酒精对象之间的信号。首先，计算EEG信号的自相关频率，作为双谱计算的基本步骤。然后，应用离散小波变换（DWT）替换通常用于双频谱计算的FFT。最后，为近似部分和细节部分都计算每个频带的相对值，从而生成RWB。所提出的方法在酒精中毒自动检测系统中使用来自UCI EEG数据库的1200个酒精中毒数据样本实施。根据实验，自相关计算中的滞后设定值显然对获得的识别率有很大的影响，即滞后的最大值最好。使用交叉验证，采用ANN分类器的RWB特征提取方法获得的最高结果达到了约90％的识别率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号