基于小波散射变换和支持向量机的心音分类

IF 0.9 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Vishwanath Madhava Shervegar
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引用次数: 0

摘要

目的:与整个心脏结构的运动相关的声音记录的表示被称为心音图(PCG)信号。在诊断这些不同的心脏疾病时,PCG信号是有帮助的。然而,由于记录PCG信号容易受到周围噪声和其他干扰信号的干扰,因此是一项复杂的任务。因此,在进行高级处理之前,需要对PCG信号进行降噪。这项工作提出了一种改进的心音分类方法,该方法利用两级低通滤波和小波阈值(WT)技术,随后使用小波散射变换进行特征提取(FE),并利用三次多项式支持向量机(SVM)技术对CVD进行进一步分类。方法:建立以PCG信号分析为核心的CVD检测计算机辅助诊断系统。首先,通过对信号进行重滤波,对使用数据库获得的原始PCG信号进行预处理。然后,通过小波变换技术去噪,去除冗余信息和噪声。从去噪后的PCG中提取小波时间散射特征。之后,通过使用支持向量机对这些特征进行病理分类。结果:在分析中,考虑了从Physionet数据集获得的PCG信号。预处理步骤采用低通巴特沃斯滤波器(LPBF)进行重低通滤波。这就消除了信号中98%的固有噪声。此外,利用小波变换技术对信号进行去噪,改善了信号强度。结果表明,该方法的最大降噪率可达99%。从PCG中提取小波散射(WS)特征,然后利用支持向量机对不同声音的PCG进行分类,准确率达到99.72%。讨论:分类精度与文献中存在的其他分类技术进行了类比。与顶级技术相比,该技术显示出有利的结果,F1分数提高了3%。指标的改进归功于预处理阶段的使用,包括低通滤波器和WT方法,WS变换(WST)和支持向量机。结论:通过与主流方法的比较研究,证明了该方法的优越性。该系统表明,冠状动脉疾病(CAD)可以通过优越的方法实现高精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Heart sound classification using wavelet scattering transform and support vector machine
OBJECTIVE: A representation of the sound recordings that are associated with the movement of the entire cardiac structure is termed the Phonocardiogram (PCG) signal. In diagnosing such diverse diseases of the heart, PCG signals are helpful. Nevertheless, as recording PCG signals are prone to several surrounding noises and other disturbing signals, it is a complex task. Thus, prior to being wielded for advanced processing, the PCG signal needs to be denoised. This work proposes an improved heart sound classification by utilizing two-stage Low pass filtering and Wavelet Threshold (WT) technique with subsequent Feature Extraction (FE) using Wavelet Scatter Transform and further classification utilizing the Cubic Polynomial Support Vector Machine (SVM) technique for CVD. METHOD: A computer-aided diagnosis system for CVD detection centered on PCG signal analysis is offered in this work. Initially, by heavily filtering the signal, the raw PCG signals obtained using the database were pre-processed. Then, to remove redundant information and noise, it is denoised via the WT technique. From the denoised PCG, wavelet time scattering features were extracted. After that, by employing SVMs, these features were classified for pathology. RESULTS: For the analysis, the PCG signal obtained from the Physionet dataset was considered. Heavy low-pass filtering utilizing a Low-Pass Butterworth Filter (LPBF) is entailed in the pre-processing step. This removed 98% of the noise inherently present in the signal. Further, the signal strength was ameliorated by denoising it utilizing the WT technique. Promising results with maximum noise removal of up to 99% are exhibited by the method. From the PCG, Wavelet Scattering (WS) features were extracted, which were later wielded to categorize the PCG utilizing SVMs with 99.72% accuracy for different sounds. DISCUSSION: The Classification accuracies are analogized with other classification techniques present in the literature. This technique exhibited propitious outcomes with a 3% improvement in the F1 score when weighed against the top-notch techniques. The improvement in the metrics is attributed to the usage of the pre-processing stage comprising of Low-pass filter and WT method, WS Transform (WST), and SVMs. CONCLUSION: The superiority of the proposed technique is advocated by the comparative investigation with prevailing methodologies. The system revealed that Coronary Artery Disease (CAD) can be implemented with superior methods to achieve high accuracy.
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来源期刊
Intelligent Data Analysis
Intelligent Data Analysis 工程技术-计算机:人工智能
CiteScore
2.20
自引率
5.90%
发文量
85
审稿时长
3.3 months
期刊介绍: Intelligent Data Analysis provides a forum for the examination of issues related to the research and applications of Artificial Intelligence techniques in data analysis across a variety of disciplines. These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, big data applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing. In particular, papers are preferred that discuss development of new AI related data analysis architectures, methodologies, and techniques and their applications to various domains.
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