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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...
485
Types Of Transformers01:16

Types Of Transformers

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
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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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Classification of Systems-I01:26

Classification of Systems-I

192
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Conduction System of the Heart01:20

Conduction System of the Heart

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The cardiac conduction system produces and transmits electrical impulses that prompt myocardial contraction, ensuring efficient heart function. This intricate system ensures that the heart beats in a coordinated and efficient manner, beginning with the atria and then the ventricles. The conduction system optimizes cardiac output by maintaining this precise sequence, which is crucial for adequate blood circulation.
This system relies on the unique properties of nodal and Purkinje cells:...
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相关实验视频

Updated: Jul 13, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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基于卷积和变压器的心声分类网络.

Jiawen Cheng1, Kexue Sun1,2

  • 1College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
概括

这项研究引入了一个新的卷积和变压器编码器神经网络 (CTENN) 用于心脏声音分类. CTENN简化了预处理,并以高精度准确检测心血管疾病 (CVD).

关键词:
中心发电系统 (CVD) 是一种变压器编码器编码器电子听觉是指电子听觉.心脏声音分类心脏声音分类神经网络的神经网络的神经网络一个维的卷积.

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科学领域:

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

背景情况:

  • 电子听觉对于诊断心血管疾病至关重要.
  • 当前的心声分类方法通常需要复杂的信号细分和特征提取.
  • 这些局限性阻碍了有效和准确的心血管疾病诊断.

研究的目的:

  • 开发一种创新和简化的方法来对心脏声音进行分类.
  • 引入一种新的卷积和变压器编码器神经网络 (CTENN),用于自动化特征提取.
  • 提高心血管疾病检测的准确性和效率.

主要方法:

  • 开发了一种名为Convolution和变压器编码器神经网络 (CTENN) 的新方法.
  • CTENN集成了1D卷积模块和变压器编码器,用于自动提取特征.
  • 这种方法绕过了传统的,精确的信号细分和特征工程的需求.

主要成果:

  • 在三个不同的数据集上,CTENN方法实现了96.4%,99.7%和95.7%的高精度.
  • 在二进制和多类心声分类任务中表现出卓越的性能.
  • 在实验评估中表现优于现有的类似方法.

结论:

  • CTENN方法为心声分类提供了一个简单而有效的解决方案.
  • 这一进步有可能显著提高心血管疾病的诊断.
  • CTENN的自动特征提取能力承诺更广泛的临床应用.