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用MFCCs和波段大贝基分析进行心脏声音分类,使用机器学习算法进行分析

Sebastian Guzman-Alfaro¹, Karen E Villagrana-Bañuelos¹, Manuel A Soto-Murillo¹

¹Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro, Zacatecas 98000, Mexico.

Diagnostics (Basel, Switzerland)

|January 10, 2026

概括

这项研究开发了一种机器学习模型,使用Mel-Frequency Cepstral Coefficients (MFCC) 和波形分析来早期检测心血管疾病. 混合方法在分类心脏声音方面取得了很高的准确性,为计算机辅助听觉提供了一个有希望的工具.

科学领域:

生物医学工程生物医学工程
心脏病学心脏病学
机器学习机器学习

背景情况:

心血管疾病 (CVD) 是全球主要的死亡原因.
可访问的早期检测工具对于管理CVD至关重要.
自动分类系统提供非侵入性诊断支持.

研究的目的:

实施和评估机器学习模型来分类正常和异常的心声.
评估混合特征提取方法,结合MFCC和波纹分析.
开发一个计算效率高的工具,用于计算机辅助听觉.

主要方法:

使用了PASCAL数据集的心脏声音记录 (正常,声,超缩).
使用Mel-Frequency Cepstral Coefficients (MFCC) 和Daubechies波束分析提取的统计特征.
训练并比较了四个分类器:SVM,后勤回归,随机森林和决策树.

主要成果:

所有分类器在区分心脏声音类别方面都表现出了显著的表现.
使用26个MFCC和Daubechies-4波段系数的支持向量机 (SVM) 模型实现了最高的准确性.
使用准确度,精度,回忆,特异性,F1得分和AUC来评估性能.

结论:

混合MFCC-Wavelet框架为心声分类提供了具有竞争力的诊断准确性.
这种方法提供了一个轻量级,可解释和计算效率高的解决方案.
这些发现支持使用该框架进行早期心血管查和计算机辅助诊断.

关键词:

在MFCC中,MFCC是最重要的.计算机辅助诊断是指计算机辅助的诊断.心脏病心脏病是一种疾病.心脏的声音听起来像是心脏的声音.机器学习是机器学习.波形分析波形分析.

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