...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Biomedical signal processing and control >Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers
【24h】

Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers

机译：通过分类器整合融合心电图的时间和形态信息的心跳分类

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

A method for the automatic classification of electrocardiograms (ECG) based on the combination of multiple Support Vector Machines (SVMs) is presented in this work. The method relies on the time intervals between consequent beats and their morphology for the ECG characterisation. Different descriptors based on wavelets, local binary patterns (LBP), higher order statistics (HOS) and several amplitude values were employed. Instead of concatenating all these features to feed a single SVM model, we propose to train specific SVM models for each type of feature. In order to obtain the final prediction, the decisions of the different models are combined with the product, sum, and majority rules. The designed methodology approaches are tested on the public MIT-131H arrhythmia database, classifying four kinds of abnormal and normal beats. Our approach based on an ensemble of SVMs offered a satisfactory performance, improving the results when compared to a single SVM model using the same features. Additionally, our approach also showed better results in comparison with previous machine learning approaches of the state-of-the-art. (C) 2018 Elsevier Ltd. All rights reserved.
机译：这项工作提出了一种基于多个支持向量机（SVM）组合的心电图自动分类方法（ECG）。该方法依赖于随后的搏动及其形态之间的时间间隔来进行ECG表征。使用基于小波，局部二进制模式（LBP），高阶统计量（HOS）和几个幅度值的不同描述符。我们建议不要针对所有类型的功能训练特定的SVM模型，而不是将所有这些功能串联起来以提供单个SVM模型。为了获得最终的预测，将不同模型的决策与乘积，总和和多数规则相结合。在公共MIT-131H心律失常数据库上对设计的方法学方法进行了测试，对四种异常和正常搏动进行了分类。与基于相同功能的单个SVM模型相比，我们基于SVM集成的方法可提供令人满意的性能，并改善了结果。此外，与以前的最新机器学习方法相比，我们的方法还显示出更好的结果。 （C）2018 Elsevier Ltd.保留所有权利。

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