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Related Concept Videos

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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Cardiopulmonary Resuscitation III: AED Use01:23

Cardiopulmonary Resuscitation III: AED Use

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Introduction to AEDAn Automated External Defibrillator (AED) is a portable medical device that analyzes the heart's rhythm and, if necessary, delivers an electrical shock to help the heart re-establish an effective rhythm during sudden cardiac arrest (SCA). SCA occurs when the heart suddenly and unexpectedly stops beating, leading to a loss of blood flow to the brain and other vital organs. In such emergencies, time is of the essence, and using an AED, combined with Cardiopulmonary...
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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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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Related Experiment Video

Updated: Aug 11, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Towards artificial intelligence-based learning health system for population-level mortality prediction using

Weijie Sun1, Sunil Vasu Kalmady1,2,3, Nariman Sepehrvand2,4

  • 1Department of Computing Science, University of Alberta, Edmonton, AB, Canada.

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Summary

Machine learning models using electrocardiogram (ECG) data can predict patient mortality risk. Deep learning models based on ECG traces outperformed traditional methods, showing value for population health and patient prognostication.

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Cardiology
  • Population Health Management

Background:

  • Linking electrocardiogram (ECG) data with administrative health records is underexplored for learning healthcare systems.
  • Machine learning (ML) models can potentially predict mortality risk using readily available ECG data.

Purpose of the Study:

  • To develop and validate ML models using ECG data to predict short-term and long-term mortality.
  • To assess the feasibility of integrating ECG data into population-level health analytics.

Main Methods:

  • Developed and validated ResNet-based Deep Learning (DL) and XGBoost (XGB) models using over 1.6 million ECGs from 244,077 patients in Alberta, Canada (2007-2020).
  • Models predicted 30-day, 1-year, and 5-year mortality.
  • Compared performance of DL models (using ECG traces) against XGB models (using ECG measurements).

Main Results:

  • ResNet DL models demonstrated good-to-excellent performance in predicting mortality (30-day AUROC: 0.843, 1-year: 0.812, 5-year: 0.798).
  • DL models based on ECG traces were superior to XGB models based on ECG measurements.
  • The study validated ECG-based DL mortality prediction models at a population level.

Conclusions:

  • ECG-based deep learning models are effective for predicting patient mortality risk.
  • These models can be integrated into clinical workflows for prognostication at the point of care.
  • Linking ECG data to administrative health data supports the development of learning healthcare systems.