...
  • 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 >Electrocardiogram beat type dictionary based compressed sensing for telecardiology application
【24h】

Electrocardiogram beat type dictionary based compressed sensing for telecardiology application

机译：基于心电图搏动型字典的压缩感知在心电学中的应用

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

Effective compression of Electrocardiogram (ECG) is a vital task in telecardiology application. Compressed sensing (CS) offers a low energy implementation based solution to the telecardiology system. In this work, an efficient beat type dictionary based ECG-CS approach is proposed. The main objective of this study is to incorporate the advantages of both beat type dictionary and non-uniform random sensing matrix for effective patient-agnostic based signal recovery. Unlike patient-specific dictionary based CS approaches, the proposed beat type dictionary offers high-quality signal recovery without the training stage for individual ECG record. The performance of the proposed scheme is evaluated using the standard MIT-BIH database. The quantitative performance matrices such as compression ratio (CR), percentage root mean square difference (PRD1), root mean square error (RMSE), signal to noise ratio (SNR) are compared with the existing CS approaches to quantify the efficacy of the proposed scheme. At PRD1 of 9%, the proposed beat type dictionary-based method presents 33.5% more CR than adaptive dictionary-based CS approach. An in-depth analysis of the results highlights that the proposed beat type dictionary based CS scheme offers an efficient solution to the patient-agnostic based signal recovery and can be served as a potential component in the computer-based automated medical system. (C) 2018 Elsevier Ltd. All rights reserved.
机译：有效地压缩心电图（ECG）是心电学应用中的重要任务。压缩传感（CS）为心电图系统提供了一种基于低能耗实现的解决方案。在这项工作中，提出了一种有效的基于节拍类型字典的ECG-CS方法。这项研究的主要目的是结合节拍型字典和非均匀随机传感矩阵的优势，实现基于患者不可知论的有效信号恢复。与基于患者特定字典的CS方法不同，建议的心跳类型字典无需单独的ECG记录的训练阶段即可提供高质量的信号恢复。使用标准的MIT-BIH数据库评估提出的方案的性能。将量化性能矩阵（例如压缩比（CR），均方根差百分比（PRD1），均方根误差（RMSE），信噪比（SNR））与现有的CS方法进行比较，以量化所提出的效果方案。在PRD1为9％的情况下，所提出的基于节拍类型字典的方法比基于自适应字典的CS方法显示的CR多33.5％。对结果的深入分析表明，基于节拍类型字典的CS方案为基于患者不可知论的信号恢复提供了有效的解决方案，并且可以用作基于计算机的自动化医疗系统中的潜在组件。 （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