Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Signals01:30

Classification of Signals

1.2K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.2K
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

11.3K
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....
11.3K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

11.3K
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...
11.3K
Instrumentation Amplifier01:25

Instrumentation Amplifier

944
An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
944
Electrocardiogram01:29

Electrocardiogram

5.0K
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...
5.0K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.3K
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...
1.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Emerging Piperazine Derivatives: Synthesis, Characterization, Biological Evaluation, Molecular Docking, and ADMET In silico Studies.

Drug metabolism and bioanalysis·2026
Same author

Biochemical and biophysical characterization of a cold-active yet highly thermostable manganese superoxide dismutase (CsMnSOD) from Camellia sinensis.

Archives of biochemistry and biophysics·2026
Same author

Delayed awakening after out-of-hospital cardiac arrest: a scoping review of definitions, determinants, and prognostic implications.

Resuscitation·2026
Same author

Association Between Vitamin D Levels and Vitamin D Receptor (VDR) Gene Polymorphisms in Nepalese Population.

Journal of Nepal Health Research Council·2026
Same author

Study of Thyroid Function and Lipid Profile in Depression Patients.

Journal of Nepal Health Research Council·2026
Same author

Efficient image steganography method using contourlet transform and geometric-based pixel encryption for enhanced security.

Scientific reports·2026

Related Experiment Video

Updated: Dec 26, 2025

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

4.2K

Detection and classification of ECG noises using decomposition on mixed codebook for quality analysis.

Pramendra Kumar1, Vijay Kumar Sharma1

  • 1Department of Computer and Communication Engineering, SCIT, Manipal University Jaipur, India.

Healthcare Technology Letters
|March 20, 2020
PubMed
Summary

This study introduces a new method for detecting and classifying electrocardiogram (ECG) noises like baseline wander and muscle artifacts. The technique achieves over 99% accuracy in identifying various ECG signal interferences.

Keywords:
AWGNECG local wavesECG noisesFantasia databaseGaussian noiseMIT-BIH polysmnographic databasePLITechnology-Boston Beth Israel Hospital arrhythmia databaseadditive white Gaussian noisedecomposed signalselectrocardiographymedical signal detectionmedical signal processingmixed codebookmuscle artefactnoisy ECG signalssignal classificationsignal decompositionsignal denoisingspectral-bound waveforms

More Related Videos

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

2.9K
Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.8K

Related Experiment Videos

Last Updated: Dec 26, 2025

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

4.2K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

2.9K
Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.8K

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cardiology

Background:

  • Electrocardiogram (ECG) signals are crucial for diagnosing heart conditions.
  • ECG signals are susceptible to various noise types, including baseline wander (BW), muscle artifact (MA), power line interference (PLI), and additive white Gaussian noise (AWGN).
  • Accurate noise detection and classification are essential for reliable ECG interpretation.

Purpose of the Study:

  • To present a robust technique for detecting and classifying common ECG noises.
  • To evaluate the performance of the proposed technique using diverse ECG databases.

Main Methods:

  • Signal decomposition using mixed codebooks with temporal and spectral-bound waveforms.
  • Sparse representation of ECG signals and simultaneous extraction of local waves and noises.
  • Application of statistical approaches and temporal features on decomposed signals for noise detection.

Main Results:

  • The proposed technique achieves an average detection accuracy of over 99% for all noise types.
  • Average sensitivity of 98.55%, positive productivity of 98.6%, and classification accuracy of 97.19% were obtained.
  • Robust performance was validated on large sets of noisy ECG signals from MIT-BIH and Fantasia databases.

Conclusions:

  • The developed technique offers a highly accurate and robust solution for ECG noise detection and classification.
  • This method can significantly improve the reliability of automated ECG analysis.
  • The findings contribute to enhanced diagnostic accuracy in clinical cardiology.