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A New Method of Sorting of Heart Sound Signal Based on Wavelet Transform and Parameter Model Method

机译:基于小波变换和参数模型法的心音信号分类新方法

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The heart sound signal, as a kind of weak biological signal under the background of strong noise, is easily subject to interference from noise of various sources. De-noising of heart sound signals, therefore, forms the primary basis for achieving non-invasive diagnosis of coronary heart disease. The paper proposes the five-level wavelet decomposition method for heart sound signals using Daubechies 6 wavelet (db6), which has yielded satisfactory results. A bi-spectrum estimation of the de-noised heart sound signals is performed based on the ARMA model, with appropriately and meticulously selected parameters. Experiments conducted on a total of 36 subjects, one half having healthy hearts and the other half afflicted with coronary heart disease, indicate that the db6-based decomposition method is capable of satisfactorily differentiating normal heart sound signals from abnormal ones.
机译:心音信号作为强噪声背景下的一种微弱的生物信号,很容易受到各种来源噪声的干扰。因此,心音信号的消噪形成了实现冠心病的非侵入性诊断的主要基础。提出了使用Daubechies 6小波(db6)对心音信号进行五级小波分解的方法,取得了令人满意的结果。基于ARMA模型,使用适当和精心选择的参数对降噪后的心音信号进行双频谱估计。对总共36位受试者进行的实验表明,基于db6的分解方法能够令人满意地区分正常的心音信号和异常的心音信号,其中一半的人心脏健康,另一半的人患有冠心病。

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