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相关概念视频

Types Of Transformers01:16

Types Of Transformers

974
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...
974
The Cardiac Cycle01:13

The Cardiac Cycle

87.9K
The heart beats rhythmically in a sequence called the cardiac cycle—a rapid coordination of contraction (systole) and relaxation (diastole).
The Process
Electrical signals—sent from the sinoatrial (SA) node in the right atrial wall to the atrioventricular (AV) node between the right atrium and right ventricle—cause both atria to simultaneously contract. When the signal reaches the AV node, it pauses for approximately a tenth of a second, allowing the atria to contract and...
87.9K
Conduction System of the Heart01:20

Conduction System of the Heart

1.0K
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:...
1.0K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

581
Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
581
Electrocardiogram01:29

Electrocardiogram

2.3K
An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...
2.3K
Three-Winding Transformers01:19

Three-Winding Transformers

224
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
224

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相关实验视频

Updated: Jul 1, 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

Published on: April 21, 2023

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心跳分类方法结合了多分支卷积神经网络和变压器.

Feiyan Zhou1,2, Jiannan Wang1,2

  • 1Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin 541004, China.

iScience
|March 14, 2024
PubMed
概括

这项研究引入了一种混合深度学习模型,将变压器和CNN结合起来,用于准确的心电图 (ECG) 失常症分类. 这种新方法有效地分析了形态和时间心电图特征,在检测SVEB和VEB心律失常时达到高准确度.

科学领域:

  • 心脏病学 心脏病学
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 检测和分类心律失常对于诊断心血管疾病至关重要.
  • 当前的深度学习方法往往难以整合形态和时间ECG特征.
  • 需要先进的模型,可以同时分析多种ECG信号特征.

研究的目的:

  • 为增强心跳分类提出混合深度学习模型.
  • 将变压器和多分支卷积神经网络 (CNN) 结合起来进行心电图分析.
  • 用MIT-BIH数据库验证模型在SVEB和VEB心律失常类的性能.

主要方法:

  • 开发了一个混合模型,集成了变压器和多分支CNN架构.
  • 实现了融合模块,将不同分类器的功能结合起来.
  • 在MIT-BIH心律失常数据库上进行了内科和外科分类协议.

主要成果:

  • 在患者内分类中实现了99.5%的整体准确性.
  • 对SVEB (92.4%,99.9%) 和VEB (98.2%,99.9%) 节律失常的高度敏感性 (Sen) 和特异性 (Spe) 已被证明.
  • 在患者间协议中获得了强有力的结果,整体准确率为98.8%和97.2%.
关键词:
人工智能的人工智能是人工智能.生物医学工程 生物医学工程

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

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

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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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结论:

  • 拟议的混合模型通过整合形态和时间心电图特征,有效地分类心律失常.
  • 该方法显示了提高自动心律失常检测的准确性和可靠性的巨大潜力.
  • 这种方法在利用深度学习来诊断心血管疾病方面提供了有前途的进展.