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相关概念视频

Heart Sounds01:15

Heart Sounds

3.2K
Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
3.2K
Classification of Signals01:30

Classification of Signals

1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Assessment of the Cardiovascular System IV: Auscultation01:25

Assessment of the Cardiovascular System IV: Auscultation

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Cardiac auscultation is a clinical skill used to assess heart function and detect abnormalities. It involves listening to heart sounds at specific anatomical locations through a stethoscope.
Normal Heart Sounds
S1 (First Heart Sound)-
S1 is made by the closure of the mitral and tricuspid valves (atrioventricular valves), marking the beginning of systole.
S2 (Second Heart Sound)-
S2 is made by the closure of the aortic and pulmonic valves (semilunar valves), marking the end of the systole.
1.7K
Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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相关实验视频

Updated: Jan 13, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

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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
PubMed
概括
此摘要是机器生成的。

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.

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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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.