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Related Concept Videos

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...
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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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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.
Parts of an ECG
An ECG utilizes electrodes on the skin...
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Pulse rhythm01:30

Pulse rhythm

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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Exercise Stress Test01:26

Exercise Stress Test

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Introduction
Exercise stress testing, commonly known as a treadmill test, is a noninvasive procedure used to evaluate cardiovascular function and diagnose heart conditions.
Definition
An exercise stress test measures the heart's response to exertion using a treadmill or stationary bicycle. Chest electrodes record the heart's electrical activity through an ECG, and blood pressure is monitored regularly.
Purposes
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Related Experiment Video

Updated: Jun 30, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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AI-based preeclampsia detection and prediction with electrocardiogram data.

Liam Butler1, Fatma Gunturkun2, Lokesh Chinthala3

  • 1Department of Internal Medicine, Section on Cardiovascular Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States.

Frontiers in Cardiovascular Medicine
|March 19, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence models can detect preeclampsia using electrocardiograms (ECGs). This AI approach offers high accuracy for early diagnosis and improved outcomes in hypertensive disorders of pregnancy.

Keywords:
AUCECG-AIdetectionelectrocardiogramgestational agepredictionpreeclampsiavalidation

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Area of Science:

  • Cardiology
  • Maternal-Fetal Medicine
  • Artificial Intelligence in Healthcare

Background:

  • Hypertensive disorders of pregnancy, including preeclampsia, cause over 76,000 maternal deaths annually.
  • Early detection and management of preeclampsia are crucial for improving maternal and fetal outcomes.
  • Current diagnostic methods may not always allow for timely intervention.

Purpose of the Study:

  • To develop and validate artificial intelligence (AI) models for the early detection and prediction of preeclampsia.
  • To assess the feasibility of using electrocardiogram (ECG) data for preeclampsia diagnosis in point-of-care settings.
  • To evaluate the predictive accuracy of AI models at various time points before clinical diagnosis.

Main Methods:

  • A modified ResNet convolutional neural network was employed to analyze 12-lead ECG signals.
  • ECG data was sourced from two large healthcare systems, with one cohort used for training and validation.
  • Models were trained on 1D raw ECG signals and tested for predictive accuracy at 30, 60, and 90 days prior to diagnosis.

Main Results:

  • The AI model achieved high cross-validated AUCs, including 0.85 on holdout data and 0.81 on an external healthcare system dataset.
  • Predictive accuracy remained high across different time windows, with AUCs of 0.92, 0.89, and 0.90 at 30, 60, and 90 days before diagnosis, respectively.
  • The model demonstrated exceptional performance for early-onset preeclampsia, yielding an AUC of 0.98.

Conclusions:

  • Artificial intelligence applied to ECG data can accurately identify preeclampsia.
  • AI-powered ECG analysis holds significant potential for improving early diagnosis and management of preeclampsia.
  • This technology could enhance point-of-care diagnostics, leading to better maternal and infant health outcomes.