• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE sensors journal >Localization of Myocardial Infarction From Multi-Lead ECG Signals Using Multiscale Analysis and Convolutional Neural Network
【24h】

Localization of Myocardial Infarction From Multi-Lead ECG Signals Using Multiscale Analysis and Convolutional Neural Network

机译：使用多尺度分析和卷积神经网络从多导联心电图信号定位心肌梗塞

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

The occlusion in one of the coronary arteries of the heart leads to the cardiac ailment, myocardial infarction (MI). The localization of MI based on the investigation of the morphology of the multi-lead electrocardiogram (ECG) is the initial task for the diagnosis of this ailment. In this paper, the multiscale convolutional neural network is proposed for the automated localization of MI ailment from multi-lead electrocardiogram (ECG) beats. The Fourier-Bessel (FB) series expansion based empirical wavelet transform (EWT) with fixed order ranges is introduced for the multiscale analysis of multi-lead ECG beat. The FB spectrum of each lead ECG beat is segregated into contiguous segments using the fixed order ranges. Furthermore, the order ranges from these contiguous segments are used to design an empirical wavelet filter bank for the extraction of subband signals from each lead ECG beat. The convolutional neural network (CNN) is used for the classification of various categories of MI as anterior MI (AMI), anterio-lateral MI (ALMI), anterio-septal MI (ASMI), inferior MI (IMI), inferio-lateral MI (ILMI), inferio-posterio-lateral MI (IPLMI) and normal sinus rhythm (NSR). The experimental results reveal that the lower-order range subband signal coupled with CNN attains higher average accuracy values of 99.92%, 99.34%, 99.95%, 99.95%, 99.91%, and 99.86% respectively, for AMI, ALMI, ASMI, IMI, ILMI, and IPLMI classes. The subband signal of multi-lead ECG beats with order range of [1-26] is highly affected during various categories of MI heart disease, and this band signal has higher performance as compared to the existing MI localization approaches.
机译：心脏冠状动脉之一的阻塞会导致心脏疾病，心肌梗塞（MI）。基于多导联心电图（ECG）形态学调查的MI定位是诊断该疾病的首要任务。本文提出了一种多尺度卷积神经网络，用于从多导心电图（ECG）搏动中自动定位心梗疾病。介绍了基于傅立叶-贝塞尔（FB）级数展开的具有固定阶数范围的经验小波变换（EWT），用于多导联ECG搏动的多尺度分析。使用固定顺序范围，将每个先导ECG搏动的FB频谱分为连续的段。此外，来自这些连续段的阶数范围用于设计经验小波滤波器组，以从每个主ECG搏动中提取子带信号。卷积神经网络（CNN）用于将MI的各种类别分类为前MI（AMI），前外侧MI（ALMI），前中隔MI（ASMI），下MI（IMI），下外侧MI （ILMI），下后外侧MI（IPLMI）和正常窦性心律（NSR）。实验结果表明，对于AMI，ALMI，ASMI，IMI，与CNN耦合的低阶子带信号分别获得了较高的平均准确度值，分别为99.92％，99.34％，99.95％，99.95％，99.91％和99.86％。 ILMI和IPLMI类。在各种类型的MI心脏病中，阶导范围为[1-26]的多导联ECG搏动的子带信号受到很大影响，并且与现有的MI定位方法相比，该带信号具有更高的性能。

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