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Related Concept Videos

Electrocardiogram01:29

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 the T...
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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. When...
Electrocardiogram Fundamentals01:28

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 to...
Pulse rhythm01:30

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

Correlation between ECG and Cardiac Cycle

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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Related Experiment Video

Updated: May 8, 2026

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
06:01

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

Continuous digital ECG analysis over accurate R-peak detection using adaptive wavelet technique.

T R Gopalakrishnan Nair1, A P Geetha, M Asharani

  • 1ARAMCO Endowed Chair-Technology PMU , KSA .

Journal of Medical Engineering & Technology
|September 11, 2013
PubMed
Summary

This study introduces an adaptive wavelet approach for automated R-peak detection in electrocardiograms (ECG). This method accurately identifies cardiac abnormalities, improving diagnostic reliability in cardiovascular healthcare.

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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

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Related Experiment Videos

Last Updated: May 8, 2026

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
06:01

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Area of Science:

  • Biomedical Engineering
  • Cardiovascular Signal Processing
  • Artificial Intelligence in Healthcare

Background:

  • Healthcare systems face challenges in achieving accurate and intelligent biomedical systems.
  • Electrocardiogram (ECG) analysis is crucial for diagnosing cardiovascular conditions, but traditional methods rely heavily on expert interpretation of raw signals.
  • Automation of ECG analysis is needed to enhance diagnostic accuracy and efficiency.

Purpose of the Study:

  • To develop a fully automated signal processing sequence for R-peak detection in ECG.
  • To address challenges in R-peak identification caused by noise, baseband wandering, and temporal variations.
  • To enable automated classification and estimation of cardio disorders using ECG signals.

Main Methods:

  • Utilized an adaptive wavelet approach for robust R-peak detection.
  • Generated a specialized wavelet optimized for R-signal identification under noisy conditions.
  • Tested the method across various ECG signal conditions, including baseband wandering and temporal R-position shifts.

Main Results:

  • Successfully designed and implemented an adaptive wavelet for R-peak detection.
  • Demonstrated accurate detection of R-peak variations even in the presence of signal interferences.
  • Validated the effectiveness of the automated signal processing sequence.

Conclusions:

  • The adaptive wavelet approach offers a reliable method for automated R-peak detection in ECG.
  • This automated process can significantly assist healthcare professionals in diagnosing and estimating cardiovascular disorders.
  • The developed method has potential for further applications in intelligent biomedical systems.