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相关概念视频

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

653
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...
653
Electrocardiogram01:29

Electrocardiogram

2.5K
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...
2.5K
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

42
Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
42
Instrument Transformers01:23

Instrument Transformers

110
Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
110
Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

388
Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...
388
Instrumentation Amplifier01:25

Instrumentation Amplifier

634
An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
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相关实验视频

Updated: Jul 27, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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一个基本的视力变压器提高了心电图的诊断性能.

Akhil Vaid1,2,3,4, Joy Jiang5,6, Ashwin Sawant7

  • 1The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. akhil.vaid@mssm.edu.

NPJ digital medicine
|June 6, 2023
PubMed
概括

一种新的变压器模型,HeartBEiT,在心电图 (ECG) 分析方面表现出色,特别是在有限的数据下. 这种人工智能方法提供了卓越的诊断性能和更好地解释复杂的心脏病状况.

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Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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科学领域:

  • 人工智能在医学中的应用
  • 心脏病学 心脏病学
  • 机器学习用于医疗保健

背景情况:

  • 电心电图 (ECG) 分析对于诊断心脏疾病至关重要.
  • 目前的深度学习模型,如卷积神经网络 (CNN),需要大量的数据集来进行有效的心电图分析.
  • 使用自然图像预训的转移学习可能不会为生物医学数据带来最佳结果.

研究的目的:

  • 开发和评估HeartBEiT,一种基于视觉的变压器模型,用于使用面具图像建模进行ECG波形分析.
  • 为了比较HeartBEiT的性能与标准CNN架构在不同的训练样本大小.
  • 通过确定相关的ECG区域来评估模型提高诊断可解释性的能力.

主要方法:

  • 开发了HeartBEiT,一种利用掩面图像建模进行心电图分析的变压器模型.
  • 预训练的HeartBEiT在一个大数据集上使用了850万个心电图.
  • 用独立的验证数据集和不同的样本大小来诊断多变性心肌病,左下心室喷射率和ST升高心肌梗塞,将HeartBEiT与CNN进行了比较.

主要成果:

  • 与CNN相比,HeartBEiT的诊断性能明显更高,特别是在较低的训练样本大小.
  • 该模型通过突出ECG中的生物相关区域来改善诊断的可解释性.
  • 变压器模型的特定领域预训练在低数据场景中表现优于自然图像预训练.

结论:

  • 针对特定领域的预训练过的变压器模型,如HeartBEiT,显示出比传统的CNN对心电图分析的性能优越,特别是在数据有限的环境中.
  • 对于心脏诊断,HeartBEiT提供了更高的准确性和细粒度可解释性.
  • 这种方法代表了应用人工智能的重大进步,用于复杂的生物医学波形分析.