Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Correlation between ECG and Cardiac Cycle

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...

Factors Influencing Heart Rate

Factors Influencing Heart Rate

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,...

Cardiac Output I:Effect of Heart Rate on Cardiac Output

Cardiac Output I:Effect of Heart Rate on Cardiac Output

Cardiac Output
Cardiac output (CO) refers to the total amount of blood ejected by one of the ventricles in liters per minute (L/min). In a resting adult, CO ranges from 5 to 6 L/min, adjusting according to the body's metabolic requirements.
Effect of Heart Rate on Cardiac Output
Cardiac output adapts to metabolic demands during stress, physical activity, or illness. The autonomic nervous system regulates heart rate via the sinoatrial node. The parasympathetic nervous system decreases heart rate...

You might also read

Related Articles

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

Sort by

Same author

Author Correction: Tree diversity is changing across tropical Andean and Amazonian forests in response to global change.

Nature ecology & evolution·2026

Same author

Tree diversity is changing across tropical Andean and Amazonian forests in response to global change.

Nature ecology & evolution·2026

Same author

Solubilization and Controlled Release Strategy of Poorly Water-Soluble Drugs.

Pharmaceuticals (Basel, Switzerland)·2022

Same author

Relationship between surface dissolved iron inventories and net community production during a marine heatwave in the subarctic northeast Pacific.

Environmental science. Processes & impacts·2022

Same author

Comprehensive Kinetics of Hydrolysis of Organotriethoxysilanes by <sup>29</sup>Si NMR.

The journal of physical chemistry. A·2019

Same author

Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation.

Computational intelligence and neuroscience·2018

Search research articles

Related Experiment Video

Updated: Jul 13, 2026

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

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

Published on: June 5, 2019

Scaling patterns of heart rate variability data.

E R Bojorges-Valdez¹, J C Echeverría, R Valdés-Cristerna

¹Electrical Engineering Department, Universidad Autónoma Metropolitana-Izt, Mexico City, Mexico.

Physiological Measurement

|August 1, 2007

Summary

Tracking local scaling patterns (SP) in heart rate variability (HRV) data offers superior classification compared to traditional fractal exponents. This advanced detrended fluctuation analysis (DFA) reveals detailed correlation structures for better diagnostic potential.

More Related Videos

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Semi-automated Optical Heartbeat Analysis of Small Hearts

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

Related Experiment Videos

Last Updated: Jul 13, 2026

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

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

Published on: June 5, 2019

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Semi-automated Optical Heartbeat Analysis of Small Hearts

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

Area of Science:

Cardiology
Biophysics
Data Analysis

Background:

Heart rate variability (HRV) analysis is crucial for understanding cardiac health.
Detrended fluctuation analysis (DFA) is a key method for assessing correlations in HRV.
Traditional DFA yields single fractal exponents (e.g., alpha(1), alpha(2)) that may oversimplify complex data.

Purpose of the Study:

To evaluate the diagnostic advantage of analyzing local scaling patterns (SP) in HRV data.
To compare the classification performance of SP against traditional DFA exponents and pNN20 statistic.
To confirm the utility of SP for characterizing the correlation structure of HRV.

Main Methods:

Applied detrended fluctuation analysis (DFA) to long-term HRV data from normal sinus rhythm subjects and congestive heart failure patients.
Tracked the local evolution of the fractal exponent alpha(x) to identify scaling patterns (SP).
Classified subjects using SP, traditional alpha(x) exponents, and the pNN20 statistic.

Main Results:

Scaling patterns (SP) achieved significantly better classification of HRV data compared to single fractal exponents (alpha(x)).
SP outperformed the pNN20 statistic in differentiating between normal sinus rhythm and congestive heart failure.
The local evolution of alpha(x) revealed detailed correlation structures not captured by single exponents.

Conclusions:

Tracking local scaling patterns (SP) provides a more sensitive assessment of HRV correlation structure than traditional DFA exponents.
SP represent a valuable advancement in HRV analysis for improved diagnostic capabilities.
The findings support the use of SP for enhanced characterization and classification of cardiac conditions via HRV.