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相关概念视频

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

559
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...
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Electrocardiogram01:29

Electrocardiogram

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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...
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Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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一个深度学习模型,从12导电心电图中推断出高的肺毛细血管压.

Daphne E Schlesinger1,2,3, Nathaniel Diamant4, Aniruddh Raghu3,5

  • 1Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA.

JACC. Advances
|June 28, 2024
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概括

一种新的深度学习模型可以从心电图 (ECG) 中非侵入性地推断出高平均肺毛细血管压 (mPCWP). 当侵入性血液动力学监测是不可行的时,这种方法提供了一个潜在的替代方案,改善了临床决策.

关键词:
这是一个ECGECGECGECGECG.深度学习是一种深度学习.肺动脉阻塞压力 肺动脉阻塞压力肺动脉曲线压力压力压力肺毛细血管形压力压力

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科学领域:

  • 心脏病学 心脏病学
  • 医疗信息学 医疗信息学
  • 人工智能在医学中的应用

背景情况:

  • 中心血动力学参数通常通过肺动脉导管测量侵入性地测量,这带有风险,并且不是普遍可用的.
  • 升高的平均肺毛细血管压 (mPCWP) 是各种心血管疾病的关键指标.

研究的目的:

  • 开发一种非侵入性的方法来识别使用12导电心电图 (ECG) 的升高mPCWP.
  • 利用深度学习从心电图数据中推断mPCWP,为侵入性测量提供潜在的替代方案.

主要方法:

  • 通过使用来自马萨诸塞州综合医院的248,955份临床记录,开发了一种深度学习模型.
  • 该模型经过训练,从ECG中推断出mPCWP>15 mmHg,其中的一部分数据用于预训练和直接mPCWP测量,用于模型开发和验证.
  • 开发了一个不可靠性得分,以量化模型预测的可靠性.

主要成果:

  • 该模型在测试组件上实现了接收器操作特征曲线 (AUC) 下的面积为0.80 ± 0.02,在持有组件上为0.79 ± 0.01.
  • 模型的性能取决于不可靠性得分,较高的得分表明性能较差 (例如,AUC在最高分位数中为0.70±0.06).

结论:

  • 通过使用深度学习,可以从心电图中非侵入性地推断出平均肺毛细血管压 (mPCWP).
  • 这些推断的可靠性可以量化,提供有价值的临床信息,当侵入性监测是不容易获得或可行的.