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Related Concept Videos

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.
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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...
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Electrocardiogram Fundamentals01:28

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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.
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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.
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Related Experiment Video

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Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Artificial Intelligence Algorithm for Screening Heart Failure with Reduced Ejection Fraction Using

Jinwoo Cho1, ByeongTak Lee1, Joon-Myoung Kwon2,3

  • 1From the Department of Research and Development, VUNO, Seoul, Korea.

ASAIO Journal (American Society for Artificial Internal Organs : 1992)
|February 25, 2021
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Summary

An AI algorithm effectively screens for heart failure with reduced ejection fraction (HFrEF) using electrocardiography (ECG). This AI tool enables early detection via standard or wearable ECG devices, preventing disease progression.

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Heart failure with reduced ejection fraction (HFrEF) is a prevalent clinical syndrome.
  • Current screening methods for HFrEF lack reliability, affordability, and early detection capabilities.
  • Effective medical therapy can modify HFrEF, underscoring the need for early diagnosis.

Purpose of the Study:

  • To develop and validate an interpretable artificial intelligence (AI) algorithm for early-stage screening of HFrEF using electrocardiography (ECG).
  • To assess the AI algorithm's performance using both 12-lead and single-lead ECGs.
  • To evaluate the algorithm's generalizability across different healthcare centers.

Main Methods:

  • A retrospective cohort study involving two hospitals.
  • Development of a convolutional neural network-based AI algorithm using 39,371 ECGs from 17,127 patients.
  • Internal validation with 3,470 ECGs and external validation with 4,362 ECGs from a separate hospital.

Main Results:

  • The AI algorithm demonstrated high accuracy in detecting HFrEF, with Areas Under the Curve (AUC) of 0.913 (12-lead ECG) and 0.874 (single-lead ECG) during internal validation.
  • External validation showed robust performance with AUCs of 0.961 (12-lead ECG) and 0.929 (single-lead ECG).
  • The AI algorithm effectively identified HFrEF using both conventional 12-lead ECGs and single-lead ECGs, suitable for wearable devices.

Conclusions:

  • An interpretable AI algorithm shows significant promise for reliable and economical early-stage HFrEF screening.
  • The AI algorithm's ability to function with single-lead ECGs opens possibilities for widespread use with wearable technology.
  • Early HFrEF detection using this AI-powered ECG approach can potentially prevent irreversible disease progression and reduce mortality.