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

Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

2.7K
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,...
2.7K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

6.4K
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...
6.4K
Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

997
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
997
Special considerations while measuring pulse01:13

Special considerations while measuring pulse

608
Assessing a patient's pulse is a fundamental skill in healthcare, but certain situations require special attention:
608
Regulation of Heart Rates01:31

Regulation of Heart Rates

1.9K
The regulation of heart rate is a complex process controlled by the autonomic nervous system (ANS), hormonal influences, and intrinsic cardiac mechanisms. The ANS has two main components: the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS).
The SNS increases heart rate through the release of norepinephrine and epinephrine, which act on beta-1 adrenergic receptors in the heart. This action increases the rate of depolarization in the sinoatrial (SA) node, the heart's...
1.9K
Cardiac Output I:Effect of Heart Rate on Cardiac Output01:19

Cardiac Output I:Effect of Heart Rate on Cardiac Output

847
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...
847

You might also read

Related Articles

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

Sort by
Same author

Improvements in medical therapy and prognosis for patients with HFrEF following the 2021 ESC HF guidelines.

ESC heart failure·2025
Same author

Texture-based probability mapping for automatic assessment of myocardial injury in late gadolinium enhancement images after revascularized STEMI.

International journal of cardiology·2025
Same author

Alterations in the autonomic and haemodynamic response to prolonged high-intensity endurance exercise in individuals with coronary artery calcification.

Experimental physiology·2024
Same author

Re-arrest immediately after return of spontaneous circulation: A retrospective observational study of in-hospital cardiac arrest.

Acta anaesthesiologica Scandinavica·2024
Same author

Stent-in-Stent Intervention for Pulmonary Vein in Stent Restenosis: A Long-Term Follow-Up Case Report.

Journal of cardiovascular electrophysiology·2024
Same author

Reply to the letter regarding the article 'Changes in 6-min walk test is an independent predictor of death in chronic heart failure with reduced ejection fraction'.

European journal of heart failure·2024

Related Experiment Video

Updated: Jul 15, 2025

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

19.9K

Optimizing support vector machines and autoregressive integrated moving average methods for heart rate variability

Jakob Svane1, Tomasz Wiktorski1, Stein Ørn2

  • 1Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger 4021, Norway.

Methodsx
|September 27, 2023
PubMed
Summary
This summary is machine-generated.

Heart rate variability (HRV) analysis during exercise is improved by new artifact correction methods. Autoregressive integrated moving average (ARIMA) and support vector regression (SVR) effectively fill data gaps caused by movement artifacts.

Keywords:
ArimaArtifactsCorrectionHRVHyperparametersOptimization of ARIMA and SVR for HRV Data CorrectionSVR

More Related Videos

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

154
Autonomic Function Following Concussion in Youth Athletes: An Exploration of Heart Rate Variability Using 24-hour Recording Methodology
05:48

Autonomic Function Following Concussion in Youth Athletes: An Exploration of Heart Rate Variability Using 24-hour Recording Methodology

Published on: September 21, 2018

10.2K

Related Experiment Videos

Last Updated: Jul 15, 2025

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

19.9K
Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

154
Autonomic Function Following Concussion in Youth Athletes: An Exploration of Heart Rate Variability Using 24-hour Recording Methodology
05:48

Autonomic Function Following Concussion in Youth Athletes: An Exploration of Heart Rate Variability Using 24-hour Recording Methodology

Published on: September 21, 2018

10.2K

Area of Science:

  • Physiology
  • Biomedical Engineering
  • Data Science

Background:

  • Heart rate variability (HRV) reflects autonomic nervous system (ANS) activity.
  • Movement during exercise introduces artifacts in HRV data, compromising analysis.
  • Existing artifact correction methods are inadequate for exercise-induced HRV data.

Purpose of the Study:

  • To evaluate Autoregressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR) for HRV artifact correction during exercise.
  • To determine optimal hyperparameters and selection criteria for ARIMA and SVR in HRV data.
  • To guide the choice of correction method based on data characteristics.

Main Methods:

  • Application of ARIMA and SVR models for HRV data artifact correction.
  • Hyperparameter optimization for ARIMA and SVR models.
  • Evaluation of model performance using appropriate criteria for gap filling and prediction.

Main Results:

  • ARIMA and SVR demonstrate potential for HRV artifact correction.
  • Methodology for selecting optimal hyperparameters and models is established.
  • Guidelines for choosing correction methods based on data are provided.

Conclusions:

  • ARIMA and SVR offer promising solutions for HRV artifact correction during physical activity.
  • The proposed methods can improve the accuracy of HRV analysis in exercise settings.
  • This work provides a framework for robust HRV data processing in dynamic conditions.