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Choice of the wavelet analyzing in the phonocardiogram signal analysis using the discrete and the packet wavelet transform

机译:使用离散和分组小波变换的心电图信号分析中的小波分析选择

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The phonocardiogram signal (PCG) can be utilized more efficiently by medical doctors when they are displayed visually, rather through a conventional stethoscope. This signal provides clinician with valuable diagnostic and prognostic information. Although the PCG signal analysis by auscultation is convenient as clinical tool, heart sound signals are so complex and non-stationary that they have a great difficulty to analyze in time or frequency domain. We have studied the extraction of features out of heart sounds in time-frequency (TF) domain for recognition of heart sounds through TF analysis. This article highlights the importance of the choice of wavelet analyzing wavelet and its order in the phonocardiogram signal analysis using the two versions of the wavelet transform: the discrete wavelet transform (DWT) and the packet wavelet transform (PWT). This analysis is based on the application of a large number of orthogonal and bi-orthogonal wavelets and whenever you measure the value of the average difference (in absolute value) between the original signal and the synthesis signal obtained by multiresolution analysis (AM). The performance of the discrete wavelet transform (DWT) and the packet wavelet transform (PWT) in the PCG signal analysis are evaluated and discussed in this paper. The results we obtain show the clinical usefulness of our extraction methods for recognition of heart sounds (or PCG signal).
机译:当医生可视地而不是通过传统的听诊器显示心音图信号(PCG)时,医生可以更有效地利用它们。该信号为临床医生提供了有价值的诊断和预后信息。尽管通过听诊进行PCG信号分析作为临床工具很方便,但是心音信号非常复杂且不稳定,以至于它们在时域或频域中分析都非常困难。我们研究了在时频(TF)域中从心音中提取特征以通过TF分析识别心音。本文重点介绍了选择小波分析小波及其顺序在使用两种小波变换形式的心电图信号分析中的重要性:离散小波变换(DWT)和分组小波变换(PWT)。此分析基于大量正交和双正交小波的应用,并且每当您测量原始信号与通过多分辨率分析(AM)获得的合成信号之间的平均差(绝对值)的值时,便会进行此分析。本文对PCG信号分析中离散小波变换(DWT)和分组小波变换(PWT)的性能进行了评估和讨论。我们获得的结果显示了我们的提取方法对心音(或PCG信号)识别的临床实用性。

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