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使用双谱技术对心音图信号进行病理判别。

Pathological discrimination of the phonocardiogram signal using the bispectral technique.

作者信息

Berraih Sid Ahmed, Baakek Yettou Nour Elhouda, Debbal Sidi Mohammed El Amine

机构信息

Biomedical Engineering Department, Faculty of Technology, Biomedical Engineering Laboratory (GBM), Tlemcen University, Tlemcen, BP 119, Algeria.

出版信息

Phys Eng Sci Med. 2020 Dec;43(4):1371-1385. doi: 10.1007/s13246-020-00943-7. Epub 2020 Nov 9.

Abstract

Phonocardiography is a dynamic non-invasive and relatively low-cost technique used to monitor the state of the mechanical activity of the heart. The recordings generated by such a technique is called phonocardiogram (PCG) signals. When shown visually, PCG signals can provide more insights of heart sounds for medical doctors. Thus, several approaches have been proposed to analyse these sounds through PCG recordings. However, due to the complexity and the high nonlinear nature of these recordings, a computer-assisted technique based on higher-order statistics HOS is shown to be, among these techniques, an important tool in PCG signal processing. The third-order spectra technique is one of these techniques; known as bispectrum, it can provide significant information to support physicians with an accurate and objective interpretation of heart condition. This technique is implemented and discussed in this paper. The implemented technique is used for the analysis of heart severity on nine different PCG recordings. These are normal, innocent murmur, coarctation of the aorta, ejection click, atrial gallop, opening snap, aortic stenosis, drum rumble, and aortic regurgitation. A unique bispectrum representation is generated for each type of heart sounds signal. Then, based on the bispectrum analysis, fifteen higher-order spectra HOS features such as the bispectral amplitude, the entropies, the moments, and the weighted center are extracted from each PCG record. The obtained HOS-features showed a well-correlated evolution with the increasing importance of heart severity leading therefore to a high potential in discriminating pathological PCG signals. One should know that, generally, classification of pathological PCG signals refers to the distinction between the presence of a pathology from its absence (binary response) while the discrimination considered in this paper provides an analogue response (value) which can vary from one pathology to another in an increasing or decreasing way.

摘要

心音图描记术是一种动态的、非侵入性且成本相对较低的技术,用于监测心脏机械活动状态。通过这种技术生成的记录称为心音图(PCG)信号。当以可视化方式呈现时,PCG信号可以为医生提供更多关于心音的见解。因此,已经提出了几种方法来通过PCG记录分析这些声音。然而,由于这些记录的复杂性和高度非线性性质,基于高阶统计量(HOS)的计算机辅助技术在这些技术中被证明是PCG信号处理中的一种重要工具。三阶谱技术就是其中之一;它被称为双谱,能够提供重要信息,以支持医生对心脏状况进行准确和客观的解读。本文对该技术进行了实现和讨论。所实现的技术用于分析九种不同PCG记录上的心脏严重程度。这些记录分别为正常、无害性杂音、主动脉缩窄、喷射性喀喇音、心房奔马律、开瓣音、主动脉瓣狭窄、隆隆样杂音和主动脉瓣反流。为每种心音信号类型生成独特的双谱表示。然后,基于双谱分析,从每个PCG记录中提取十五个高阶谱(HOS)特征,如双谱幅度、熵、矩和加权中心。所获得的HOS特征显示出随着心脏严重程度重要性的增加而呈现出良好的相关性演变,因此在区分病理性PCG信号方面具有很高的潜力。应该知道,一般来说,病理性PCG信号的分类是指区分病理状态的存在与否(二元响应),而本文所考虑的辨别提供的是一种类似物响应(值),它可以因不同病理状态而以增加或减少的方式变化。

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