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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

3.4K
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...
3.4K
Pulse rhythm01:30

Pulse rhythm

955
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...
955
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

8.8K
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...
8.8K
Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

395
Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
395
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

909
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...
909
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

4.9K
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....
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Related Experiment Video

Updated: Sep 28, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Cardiac disease detection from ECG signal using discrete wavelet transform with machine learning method.

M Mohamed Suhail1, T Abdul Razak1

  • 1Department of Computer Science, Jamal Mohamed College, Tiruchirappalli, India.

Diabetes Research and Clinical Practice
|March 28, 2022
PubMed
Summary

This study introduces an automated framework for detecting heart disease using electrocardiogram (ECG) data and nonlinear analysis. The developed model achieves high accuracy, improving early diagnosis of cardiovascular conditions.

Keywords:
Cardiac diseaseECGNonlinear vector decomposed neural networkTraining and testing

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Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Artificial Intelligence in Medicine

Background:

  • Cardiac disease is a leading global cause of mortality.
  • Early diagnosis of cardiovascular problems is crucial for prevention.
  • Electrocardiogram (ECG) is a key diagnostic tool for heart conditions.

Purpose of the Study:

  • To develop an automated framework for heart disease detection using ECG analysis.
  • To integrate multi-field extraction and nonlinear analysis for improved diagnosis.
  • To create a model for future diagnosis of cardiovascular disease via ECG and symptom-based detection.

Main Methods:

  • Utilized Discrete Wavelet Transform (DWT) for ECG signal preprocessing to remove noise.
  • Employed Nonlinear Vector Decomposed Neural Network (NVDNN) for heart disease prediction.
  • Trained the neural network with thirteen clinical features for classification.

Main Results:

  • The system achieved high performance metrics: 92.0% sensitivity, 89.33% specificity, and 90.67% accuracy.
  • Modules were successfully implemented, trained, and tested on UCI and PhysioNet data repositories.
  • The approach demonstrated effectiveness in identifying cardiac illness through ECG categorization.

Conclusions:

  • The proposed framework effectively identifies complex nonlinear correlations in ECG data.
  • This approach enhances ECG classification accuracy for more precise cardiac disease diagnosis.
  • The method offers superior accuracy in ECG categorization for identifying cardiac illness compared to other techniques.