• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Mediterranean Conference on Medical and Biological Engineering and Computing >Comparison between Artificial Neural Networks and Discriminant Functions for Automatic Detection of Epileptiform Discharges
【24h】

Comparison between Artificial Neural Networks and Discriminant Functions for Automatic Detection of Epileptiform Discharges

机译：人工神经网络与癫痫发出自动检测判别功能的比较

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This study presents the performance analysis between two classifiers when they are used together with mimetic analysis based morphological features to develop a method for automatic detection of epileptiform discharges in EEG signals. We applied mimetic analysis in the form of extracting a set of morphological descriptors, which in this study represent a set of parameters related to morphology features of the EEG waveform. The two tested classifiers are Discriminant Functions (DF) and Artificial Neural Networks (ANN). The DFs are obtained from Discriminant Analysis and are frequently applied in pattern classification problems such as automatic identification epileptiform discharges. On the other hand, the ANNs are an Artificial Intelligence tool commonly used in pattern recognition methods and systems. Simulations showed average efficiency of 84%, sensitivity of 86% and specificity of 82%. While the neural networks presented better sensitivity values, the discriminant functions had better specificity results. Also, it was noticed that the efficiency values for small sized classifiers were equivalent but as the classifier's size increased the neural networks exhibited better results than the discriminant functions.
机译：本研究在与模拟性分析的形态特征一起使用时，在两个分类器之间进行了性能分析，以开发一种用于在脑电图中自动检测癫痫型放电的方法。我们以提取一组形态描述符的形式应用模拟分析，这在该研究中表示与EEG波形的形态特征有关的一组参数。两个测试的分类器是判别函数（DF）和人工神经网络（ANN）。 DFS是从判别分析中获得的，并且经常应用于模式分类问题，例如自动识别癫痫发出。另一方面，ANN是一种常用于模式识别方法和系统的人工智能工具。模拟显示平均效率为84％，灵敏度为86％，特异性为82％。虽然神经网络呈现更好的灵敏度值，但判别功能具有更好的特异性结果。此外，注意到小尺寸分类器的效率值是等同的，但随着分类器的尺寸增加，神经网络呈现比判别功能更好的结果。

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