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Related Concept Videos

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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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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Electrophysiology of Normal Cardiac Rhythm01:19

Electrophysiology of Normal Cardiac Rhythm

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The normal cardiac rhythm is a synchronized electrical activity that facilitates the regular and coordinated contraction of the heart muscle. This process is essential for efficient blood circulation throughout the body. The fundamental elements involved in establishing and maintaining this rhythm include the unique electrical properties of cardiac muscle cells, the sinoatrial (SA) node's pacemaker function, the specialized conducting system, and the ionic mechanisms underlying each phase...
8.6K
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
11.7K
Cardiac Action Potential01:30

Cardiac Action Potential

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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: Jan 2, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram.

Zhi Li1, Dengshi Zhou2, Li Wan3

  • 1College of Electronic and Information Engineering, Sichuan University, Chengdu 610065, China; Key Laboratory of Wireless Power Transmission of Ministry of Education, Sichuan University, 610065, China.

Journal of Electrocardiology
|December 9, 2019
PubMed
Summary

A novel deep learning model accurately classifies cardiac arrhythmia using electrocardiogram (ECG) data. This advanced ResNet algorithm aids clinicians in diagnosing heart conditions and reducing mortality.

Keywords:
2-LeadArrhythmiaDeep learningECG signalsHeartbeat classificationResidual convolutional neural network

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Electrocardiogram (ECG) is a simple, non-invasive tool for diagnosing heart disease, including various types of arrhythmia.
  • Accurate arrhythmia detection is crucial for preventing heart disease progression and reducing mortality.

Purpose of the Study:

  • To develop a novel deep learning method for classifying cardiac arrhythmia using ECG signals.
  • To evaluate the performance of a deep residual network (ResNet) for automated heartbeat identification.

Main Methods:

  • A 31-layer, one-dimensional (1D) residual convolutional neural network (ResNet) was developed.
  • The algorithm incorporates residual blocks with 1D convolution, batch normalization, ReLU activation, and identity shortcut connections.
  • Two-lead ECG signals were utilized in combination with the deep learning model to identify five distinct heartbeat types.

Main Results:

  • The deep ResNet model achieved high classification performance on single-lead ECG data, with an average accuracy of 99.06%, sensitivity of 93.21%, and positive predictivity of 96.76%.
  • On 2-lead ECG datasets, the model demonstrated excellent results, achieving 99.38% accuracy, 94.54% sensitivity, and 98.14% specificity.
  • The proposed method shows significant potential for automated cardiac arrhythmia detection.

Conclusions:

  • The developed deep learning method, based on ResNet, effectively classifies cardiac arrhythmia using ECG data.
  • This approach can serve as a valuable adjunct tool to assist clinicians in diagnosing heart conditions.