...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computers in Biology and Medicine >Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals.
【24h】

Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals.

机译：基于PCG信号的小波变换的基于心脏瓣膜疾病的神经网络分类的心音再现。

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

Cardiac auscultatory proficiency of physicians is crucial for accurate diagnosis of many heart diseases. Plenty of diverse abnormal heart sounds with identical main specifications and different details representing the ambient noise are indispensably needed to train, assess and improve the skills of medical students in recognizing and distinguishing the primary symptoms of the cardiac diseases. This paper proposes a versatile multiresolution wavelet-based algorithm to first extract the main statistical characteristics of three well-known heart valve disorders, namely the aortic insufficiency, the aortic stenosis, and the pulmonary stenosis sounds as well as the normal ones. An artificial neural network (ANN) and statistical classifier are then applied alternatively to choose proper exclusive features. Both classification approaches suggest using Daubechies wavelet filter with four vanishing moments within five decomposition levels for the most prominent distinction of the diseases. The proffered ANN isa multilayer perceptron structure with one hidden layer trained by a back-propagation algorithm (MLP-BP) and it elevates the percentage classification accuracy to 94.42. Ultimately, the corresponding main features are manipulated in wavelet domain so as to sequentially regenerate the individual counterparts of the underlying signals.
机译：医生的心脏听诊能力对许多心脏病的准确诊断至关重要。训练，评估和提高医学生识别和区分心脏病的主要症状的技能是必不可少的，它们具有相同的主要规格和代表环境噪声的不同细节，需要多种多样的异常心音。本文提出了一种基于多分辨率小波的通用算法，以首先提取三种众所周知的心脏瓣膜疾病的主要统计特征，即主动脉瓣关闭不全，主动脉瓣狭窄，肺动脉瓣狭窄以及正常声音。然后，可替代地使用人工神经网络（ANN）和统计分类器来选择适当的专有特征。两种分类方法都建议使用Daubechies小波滤波器，在五个分解级别内具有四个消失矩，以最突出地区分疾病。提供的ANN是一种多层感知器结构，具有一个通过反向传播算法（MLP-BP）训练的隐藏层，并且将百分比分类精度提高到94.42。最终，在小波域中操纵相应的主要特征，以便顺序地再生基础信号的各个对应部分。

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