基于EMD的平均小波系数法心电信号去噪

Nabi Zahia, Ouali Mohammed Assam, Ladjal Mohamed, Bennacer Hamza
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引用次数: 0

摘要

心电图(ECG)是解释和识别心血管疾病的主要工具之一。心电信号经常受到各种噪声的干扰,这些噪声改变了原始信号,降低了信号的质量。心电信号滤波使心脏病专家能够准确评估心脏健康状况。本文提出了一种基于EMD(经验模态分解)和AWC(平均小波系数法)的心电信号去噪方法。所建议的技术背后的基本思想最初包括在有限数量的IMF(固有模式函数)上解构有噪声的心电信号数据,然后使用AWC技术计算每个IMF的赫斯特指数。最后,经过阈值处理后,通过添加所有的imf来恢复干净的心电信号,排除那些被认为是噪声的部分。使用MIT-BIH数据库对建议的方法进行了实验评估。实验结果表明,该方法能有效地从噪声数据样本中提取心电信号。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EMD Based Average Wavelet Coefficient Method for ECG Signal Denoising
Electrocardiogram (ECG) is one of the main tools to interpret and identify cardiovascular disease. ECG signals are frequently submitted to various noises, which alter the original signal and reduce its quality. ECG signal filtering enables cardiologists to assess heart health accurately. The present paper presents a newfound approach for ECG signal denoising built on two techniques which are EMD (Empirical Mode Decomposition) and AWC (Average Wavelet Coefficient method). The basic idea behind the suggested technique initially consists of deconstructing noisy ECG signal data on a restricted number of IMFs (Intrinsic Mode Functions) and then using the AWC technique to compute each IMF’s Hurst exponent. Finally, after a thresholding operation, the clean ECG signal is recovered by adding all IMFs, excluding those considered parts of noise. The suggested approach is assessed over experiments using the MIT-BIH databases. The experimental results reveal that the suggested method efficiently extracts ECG signals from noisy data samples.
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