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相关概念视频

Heart Sounds01:15

Heart Sounds

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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)...
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Classification of Signals01:30

Classification of Signals

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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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Conduction System of the Heart01:19

Conduction System of the Heart

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Autorhythmicity is a term that refers to the heart's inherent ability to generate electrical signals and instigate muscle contractions. This self-regulating conduction system within the heart consists of two key components: the pacemaker cells and specialized conducting cells.
The pacemaker cells are located in two primary nodes: the sinoatrial (SA) node and the atrioventricular (AV) node. The SA node pacemaker cells can autonomously depolarize, triggering an action potential that leads to the...
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Assessment of the Cardiovascular System IV: Auscultation01:25

Assessment of the Cardiovascular System IV: Auscultation

683
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.
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Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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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: Sep 18, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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基于卷积神经网络的心脏声音分类,并具有卷积阻断注意模块.

Ximing Huai1, Lei Jiang2,3, Chao Wang1

  • 1Ningbo Key Laboratory of Intelligent Manufacturing of Textiles and Garments, Zhejiang Fashion Institute of Technology, Ningbo, Zhejiang, China.

Frontiers in physiology
|June 20, 2025
PubMed
概括

这项研究增强了用于诊断心血管疾病 (CVD) 的心声分类,使用了基于注意力的新型卷积神经网络 (CNN). 改进的模型实现了高精度,显示出临床应用的前景.

关键词:
这就是为什么CBAM是CBAM.注意力机制注意力机制卷积块注意力模块的注意力模块卷积神经网络是一种卷积神经网络.心脏声音分类心脏声音分类医疗信号处理 医疗信号处理

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Semi-automated Optical Heartbeat Analysis of Small Hearts
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Semi-automated Optical Heartbeat Analysis of Small Hearts

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相关实验视频

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Semi-automated Optical Heartbeat Analysis of Small Hearts
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科学领域:

  • 生物医学工程 生物医学工程
  • 人工智能在医学中的应用
  • 心脏病学 心脏病学

背景情况:

  • 心血管疾病 (CVD) 是全球死亡的主要原因之一.
  • 对心血管疾病而言,准确有效的诊断工具至关重要.
  • 目前的诊断方法可能缺乏早期检测所需的精度.

研究的目的:

  • 使用深度学习开发一个增强的心声分类框架.
  • 将卷积块注意模块 (CBAM) 与卷积神经网络 (CNN) 集成.
  • 为了评估基于注意力的CNN用于分类心脏声音的性能.

主要方法:

  • 利用了PhysioNet CinC 2016数据集中的心脏声音记录.
  • 将处理的音频数据转化为光谱图以进行分析.
  • 系统地评估了12个具有不同CBAM配置的CNN模型.

主要成果:

  • 最优的CNN模型与CBAM集成实现了98.66%的准确性,主要的数据集.
  • 在一个独立的PhysioNet 2022数据集上进行验证,准确率为95.6%,AUC为96.29%.
  • T-SNE可视化显示了明确的类分离,表明有效的特征提取.

结论:

  • 选择性整合CBAM显著改善了CNN在心脏声音分类中的表现.
  • 基于注意力的架构对于医疗信号分类是有效的.
  • 开发的框架显示了在诊断心血管疾病方面实际临床应用的潜力.