基于调制综经验模态分解的心电信号t波识别研究

IF 0.5 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Chun-Hsiang Huang, T. Hsiao
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引用次数: 1

摘要

心血管疾病是全球死亡的主要原因。为了诊断心脏病,自动识别心电图的t波是必要的。经验模态分解(EMD)可以用来分解非线性和非平稳信号。然而,使用EMD分解心电信号可能会导致模式混合问题。本研究提出了调制EEMD (mEEMD)作为一种解决方案,它可以在几乎没有噪声影响的情况下解决模态混合问题。此外,mEEMD具有较少问题的边界副作用,并且不会引起任何相移。t波起始和偏移识别的灵敏度分别为[公式:见文]和[公式:见文]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Toward T-Wave Recognition of ECG Signals Through Modulated Ensemble Empirical Mode Decomposition
The cardiovascular diseases are the major cause of death globally. To diagnose heart disease, automatic recognition of ECG’s T-wave is necessary. Empirical mode decomposition (EMD) can be used to decompose nonlinear and nonstationary signals. However, using EMD to decompose ECG potentially leads to a mode mixing problem. This study proposes modulated EEMD (mEEMD) as a solution, which can solve mode mixing problems with almost no influence from noise. Furthermore, the mEEMD has a less problematic boundary side effect and does not cause any phase shift. The sensitivity of T-wave onset and offset recognition is [Formula: see text] and [Formula: see text].
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来源期刊
Advances in Data Science and Adaptive Analysis
Advances in Data Science and Adaptive Analysis MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
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