Optimum ECG Signal Filtering Based on Wavelet Transformation

B. Saidov, V. Telezhkin
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Abstract

The development of digital signal processing and microprocessor technology creates conditions for improving methods for diagnosing the functional state of organs. Wavelet analysis is a modern and promising method of information processing. In order to determine the effective optimal filtering of the electrocardiography signal based on the wavelet transform, wavelet filtering was performed using wavelets of different families, the efficiency of using different levels of decomposition, me¬thods for calculating the threshold and types of the threshold function was investigated. Aim. Determination of effective optimal filtering of electrocardiography signal based on wavelet transform. Materials and methods. Cardiograms were taken for analysis. Then they were digitized and entered into a computer for processing. A program was written in the Matlab environment that implements continuous and discrete wavelet transform. Results. As a result of the research, 56 combinations of noise reduction parameters were tested for three noise levels. It was found that the maximum degree of signal purification from noise was obtained using the Coiflets 5 wavelet using a rigid thresholding method, with a heuristic method for calculating the threshold value. Wavelet Simlet 8 has lower correlation coefficient values than Coiflets 5, at 35 dB the best result is 97%, the noise level is 40 dB the best result is 98.7%, the noise level is 45 dB the best result is 99.3%, which is generally negligible differs from the correlation coefficients of the wavelet Coiflets 5. Conclusion. As a result of the study, the first and the present work, the following conclusions were made: the optimal level of the wavelet decomposition of the ECG signal N = 2; the maximum degree of signal cleaning from noise was obtained using the Coiflets 5 wavelet using a rigid thresholding method, with a heuristic method for calculating the threshold value; Simlet 8 wavelet using a soft thresholding method with a minimax thresholding method also shows noteworthy results, slightly inferior to Coiflets 5 wavelet results.
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基于小波变换的心电信号优化滤波
数字信号处理和微处理器技术的发展为器官功能状态诊断方法的改进创造了条件。小波分析是一种很有前途的现代信息处理方法。为了确定基于小波变换的心电图信号有效的最优滤波方法,采用不同科的小波进行小波滤波,研究了不同层次分解的效率、阈值的计算方法和阈值函数的类型。的目标。基于小波变换的心电图信号有效最优滤波确定。材料和方法。进行心电图分析。然后将它们数字化并输入计算机进行处理。在Matlab环境下编写了实现连续和离散小波变换的程序。结果。研究的结果是,在三个噪声水平下测试了56种降噪参数组合。结果表明,Coiflets 5小波采用刚性阈值法获得了最大的噪声信号净化程度,并采用启发式方法计算阈值。小波Simlet 8的相关系数值低于Coiflets 5,在35 dB时的最佳结果为97%,噪声级为40 dB时的最佳结果为98.7%,噪声级为45 dB时的最佳结果为99.3%,与Coiflets 5的相关系数差异一般可以忽略。结论。通过对第一篇和本工作的研究,得出以下结论:心电信号的小波分解的最优水平N = 2;采用刚性阈值法对Coiflets 5小波进行最大程度的信号去噪,并采用启发式方法计算阈值;Simlet 8小波使用软阈值法和极大极小阈值法也显示出值得注意的结果,略逊于Coiflets 5小波结果。
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