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Electrocardiogram

Electrocardiogram

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...

Electrocardiogram Fundamentals

Electrocardiogram Fundamentals

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...

Pulse rhythm

Pulse rhythm

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...

Cardiac Action Potential

Cardiac Action Potential

Cardiac action potentials are essential for proper heart function, enabling the rhythmic contractions needed for adequate blood circulation. Nodal cells and Purkinje fibers, specialized for electrical conduction, generate these action potentials.
The cardiac action potential process involves a series of phases characterized by the movement of ions across the cardiac cell membranes, leading to the depolarization and repolarization of the cardiac myocytes.
Ionic Basis of Cardiac Action Potentials

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Related Experiment Video

Updated: May 21, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Sudden cardiac arrest prediction via deep learning electrocardiogram analysis.

Matt T Oberdier¹, Luca Neri^1,2, Alessandro Orro³

¹Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21205, USA.

European Heart Journal. Digital Health

|March 20, 2025

Summary

Deep learning models analyzing electrocardiograms (ECGs) show promise for predicting sudden cardiac arrest (SCA). Models using ECG data up to a year before the event demonstrated predictive value, highlighting potential for early detection.

Keywords:

Artificial intelligence Cardiac arrest Convolutional neural network Deep learning Electrocardiogram Prediction

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Last Updated: May 21, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

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Published on: May 23, 2021

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

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Published on: December 11, 2019

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Area of Science:

Cardiology
Artificial Intelligence
Biomedical Engineering

Background:

Sudden cardiac arrest (SCA) is a critical medical event with high mortality, often occurring unexpectedly.
Electrocardiograms (ECGs) provide vital cardiac electrical activity data.
Deep learning offers advanced analytical capabilities for complex biological signals.

Purpose of the Study:

To explore the potential of deep learning models using ECG data for predicting SCA.
To assess the impact of time intervals between ECG recording and SCA event on prediction accuracy.
To identify key ECG features utilized by deep learning models for SCA prediction.

Main Methods:

Utilized a public dataset of 12-lead ECGs from individuals with and without SCA.
Developed deep convolution neural network models incorporating age, sex, and ECG data.
Analyzed ECGs recorded at various intervals prior to SCA (within 1 day, 1 day to 1 month, 1 month to 1 year).
Employed gradient-weighted class activation mapping to interpret model predictions.

Main Results:

A base model using ECGs within 1 day of SCA achieved an area under the curve (AUC) of 0.77, but with low specificity (31% at 95% sensitivity).
Models incorporating ECG data from 1 day to 1 year prior to SCA demonstrated significant predictive capabilities.
Deep learning models primarily relied on the QRS complex of the ECG for SCA prediction.

Conclusions:

Deep learning analysis of ECGs is a viable approach for SCA screening.
Predictive performance is influenced by the proximity of ECG recording to the SCA event, with data up to a year prior showing value.
The QRS complex is a critical component of ECG data for deep learning-based SCA prediction.