Signal denoising by empirical mode decomposition

Ashish Rohila, R. Patel, V. K. Giri
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引用次数: 2

Abstract

The ECG is an important clinical tool to diagnose or to monitor various cardiac diseases. Like other electrical signals, the ECG signal also corrupted by various kinds of noise or artifacts which affect diagnosis interpretation and leads to erroneous results. In order to diagnose a cardiac abnormality, an accurate and noiseless ECG signal is required. In this paper, a denoising algorithm based on Empirical Mode Decomposition (EMD) has been proposed. The performance of present algorithm has been compared with other established denoising methods. The comparison has been performed on the basis of statistical tools and morphological study of the signals. The obtained results show that the present algorithm performs better than other denoising techniques.
基于经验模态分解的信号去噪
心电图是诊断和监测各种心脏疾病的重要临床工具。与其他电信号一样,心电信号也会受到各种噪声或伪影的干扰,从而影响诊断解释,导致错误的结果。为了诊断心脏异常,需要准确、无噪声的心电信号。本文提出了一种基于经验模态分解(EMD)的噪声去噪算法。将该算法的性能与其他已有的去噪方法进行了比较。在统计工具和形态学研究的基础上进行了比较。实验结果表明,该算法的降噪效果优于其他降噪技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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