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

Variability: Analysis01:11

Variability: Analysis

613
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
613
Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

7.1K
The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
7.1K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

14.1K
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...
14.1K
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

17.2K
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....
17.2K
Dysrhythmias III: Characteristics of Dysrhythmias01:29

Dysrhythmias III: Characteristics of Dysrhythmias

630
Dysrhythmias, also known as arrhythmias, are irregular heart rhythms that result from abnormal electrical activity in the heart, affecting its ability to circulate blood efficiently. Tachyarrhythmias, a subset of dysrhythmias, are characterized by abnormally fast heart rates exceeding 100 beats per minute. Here are some types of tachyarrhythmias with their distinct ECG features:Sinus Tachycardia:Sinus tachycardia presents a regular heart rhythm with an increased rate of 101-180 beats per...
630
Electrocardiogram01:29

Electrocardiogram

7.4K
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...
7.4K

You might also read

Related Articles

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

Sort by
Same author

Relationship between occupational stress injury score and simulated patient-care scenario performance among experienced paramedics.

Work (Reading, Mass.)·2022
Same author

Occupational health profile of Canadian Maritimes truck drivers.

Work (Reading, Mass.)·2020
Same author

Physiological responses during paramedics' simulated driving tasks.

Work (Reading, Mass.)·2020
Same author

Exploration of the health status of experienced New Brunswick paramedics.

Work (Reading, Mass.)·2020
Same author

Clustering Continuous Wavelet Transform Characteristics of Heart Rate Variability through Unsupervised Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020
Same author

Exploring simulated driving performance among varsity male soccer players.

Traffic injury prevention·2019
Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026
Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026
Same journal

Integrating stemness and epithelial-mesenchymal transition signatures with machine learning identifies RUNX1 as a therapeutic vulnerability in colorectal cancer.

Computers in biology and medicine·2026
Same journal

Differential regional textural attributes of tongue in normal and acidity patients in the light of traditional Chinese medicine.

Computers in biology and medicine·2026
Same journal

SC-MSDNet: Spatial-consistent multi-view self-distillation for retinal OCT classification.

Computers in biology and medicine·2026
Same journal

Blind source separation of nonlinearly mixed plant leaf electrical signals using polynomial-mapped FastICA.

Computers in biology and medicine·2026
See all related articles

Related Experiment Video

Updated: Mar 15, 2026

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
08:12

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

Published on: June 5, 2019

20.6K

Assessing heart rate variability through wavelet-based statistical measures.

Mark P Wachowiak1, Dean C Hay2, Michel J Johnson3

  • 1Department of Computer Science and Mathematics, Nipissing University, North Bay, ON, Canada.

Computers in Biology and Medicine
|September 7, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel wavelet transform method for analyzing electrocardiogram (ECG) signals. This approach reveals greater heart rate variability (HRV) during lower body negative pressure, offering new insights beyond traditional HRV metrics.

Keywords:
Continuous wavelet transformElectrocardiogramHeart rate variabilityInformation theory

More Related Videos

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

15.3K
Examining Changes in HRV and Emotion Following Artmaking with Three Different Art Materials
06:24

Examining Changes in HRV and Emotion Following Artmaking with Three Different Art Materials

Published on: January 11, 2020

6.8K

Related Experiment Videos

Last Updated: Mar 15, 2026

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
08:12

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

Published on: June 5, 2019

20.6K
Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

15.3K
Examining Changes in HRV and Emotion Following Artmaking with Three Different Art Materials
06:24

Examining Changes in HRV and Emotion Following Artmaking with Three Different Art Materials

Published on: January 11, 2020

6.8K

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Heart rate variability (HRV) is crucial for diagnosing cardiac conditions.
  • Traditional time and frequency domain analyses have limitations in capturing dynamic HRV changes.
  • Time-frequency methods, like wavelet transforms, offer advanced HRV analysis.

Purpose of the Study:

  • To propose a complementary computational approach using continuous wavelet transforms (CWT) directly on ECG signals.
  • To quantify time-varying frequency changes in lower HRV bands.
  • To compare these novel findings with standard HRV metrics under resting and lower body negative pressure (LBNP) conditions.

Main Methods:

  • Application of continuous wavelet transforms (CWT), including standard Morlet wavelet and a novel windowed complex sinusoid transform, directly to ECG signals.
  • Analysis of time-varying frequency changes in lower frequency bands (0.5-1.25Hz).
  • Comparison of wavelet analysis results with standard HRV metrics (e.g., RMSSD, SDSD, LF/HF) using statistical and information-theoretic measures.

Main Results:

  • Standard HRV metrics confirmed expected lower variability under LBNP due to sympathetic activity (p<0.05).
  • Wavelet analysis revealed significantly higher variability in ECG signals under LBNP, measured by frequency band roughness (Morlet CWT: p=0.041) and entropy (Morlet CWT: p=0.001).
  • Approximate entropy also showed significantly higher variability via Morlet CWT (p=0.004).

Conclusions:

  • Time-frequency analysis of ECG signals, particularly using wavelet transforms, provides complementary insights into HRV.
  • This advanced analysis can reveal complex HRV dynamics not fully captured by traditional methods.
  • The proposed wavelet approach enhances the understanding of cardiovascular autonomic regulation.