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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.
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

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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.
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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.
Parts of an ECG
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Dysrhythmias V: Evaluating Dysrhythmias01:30

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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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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.
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Related Experiment Video

Updated: Nov 22, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction.

Itzhak Zachi Attia1, Andrew S Tseng1, Ernest Diez Benavente2

  • 1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.

International Journal of Cardiology
|January 5, 2021
PubMed
Summary
This summary is machine-generated.

A novel artificial intelligence electrocardiogram algorithm (AI-ECG) effectively detects left ventricular systolic dysfunction (LVSD) in an external population. This AI-ECG shows robust performance, indicating its potential for broader clinical application in identifying LVSD.

Keywords:
Artificial intelligenceElectrocardiogramLeft ventricular systolic dysfunctionMachine learning

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Left ventricular systolic dysfunction (LVSD) is linked to increased morbidity and mortality, even in asymptomatic individuals.
  • An AI-ECG algorithm was previously developed using a large dataset from the Mayo Clinic to detect LVSD.
  • External validation is crucial to assess the generalizability of AI-ECG for LVSD detection.

Purpose of the Study:

  • To validate a novel AI-ECG algorithm for detecting LVSD in an independent, external population.
  • To assess the performance of the AI-ECG algorithm in a population distinct from its derivation cohort.
  • To evaluate the algorithm's accuracy and reliability in identifying LVSD.

Main Methods:

  • An external validation study was conducted using data from the Know Your Heart Study in Russia.
  • The study included adults aged 35-69 years with available ECG and transthoracic echocardiography.
  • LVSD was defined as left ventricular ejection fraction ≤ 35%.

Main Results:

  • The AI-ECG demonstrated robust performance in detecting LVSD, with an area under the receiver operating curve of 0.82.
  • In the external validation cohort of 4277 subjects, the AI-ECG achieved 26.9% sensitivity and 97.4% specificity at a probability cut-off of 0.256.
  • The prevalence of LVSD in this external population (0.6%) was significantly lower than in the original derivation study (7.8%).

Conclusions:

  • The AI-ECG algorithm demonstrated robust test performance for LVSD detection in a highly distinct external population.
  • Population-specific cut-offs for the AI-ECG algorithm may be required for optimal clinical implementation.
  • Variations in population characteristics and data quality could influence the algorithm's performance.