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Related Concept Videos

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

602
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...
602
Electrocardiogram01:29

Electrocardiogram

2.4K
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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Updated: Jul 9, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation

David Ouyang1, John Theurer2, Nathan R Stein2

  • 1Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

The Lancet. Digital Health
|December 8, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning analysis of electrocardiograms (ECGs) can predict postoperative mortality risk more accurately than traditional methods. This AI tool enhances risk stratification for various medical procedures, improving patient safety.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Cardiovascular Diagnostics
  • Predictive Analytics in Healthcare

Background:

  • Current preoperative risk assessments are insufficient for identifying patients at risk of postoperative mortality.
  • Electrocardiograms (ECGs) contain hidden risk markers that deep learning can analyze.
  • There is a need for improved prognostic models for postoperative mortality prediction.

Purpose of the Study:

  • To develop and validate a deep-learning-based prognostic model for predicting postoperative mortality.
  • To assess the model's performance against the Revised Cardiac Risk Index (RCRI).
  • To evaluate the algorithm's generalizability across different healthcare systems and procedure types.

Main Methods:

  • A deep-learning algorithm was developed using preoperative ECGs from a derivation cohort (Cedars-Sinai Medical Center).
  • The algorithm leveraged ECG waveform signals to discriminate postoperative mortality.
  • Model performance was evaluated using Area Under the Receiver Operating Characteristic Curve (AUC) in internal and external test cohorts, compared to RCRI.

Main Results:

  • The deep-learning algorithm achieved an AUC of 0.83 in the internal test cohort, outperforming the RCRI score (AUC 0.67).
  • The algorithm demonstrated strong performance in two external healthcare systems (AUCs 0.79 and 0.75).
  • High-risk patients identified by the algorithm had a significantly higher odds ratio for mortality (8.83) compared to those with high RCRI scores (2.08).

Conclusions:

  • Deep learning interpretation of preoperative ECGs significantly improves the discrimination of postoperative mortality risk.
  • The algorithm is effective across various procedures including cardiac surgery, non-cardiac surgery, and catheterization lab procedures.
  • This AI tool can provide valuable additional information for clinical decision-making and patient risk stratification.