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

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

8.3K
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...
8.3K
Assessment of the Cardiovascular System IV: Auscultation01:25

Assessment of the Cardiovascular System IV: Auscultation

646
Cardiac auscultation is a clinical skill used to assess heart function and detect abnormalities. It involves listening to heart sounds at specific anatomical locations through a stethoscope.
Normal Heart Sounds
S1 (First Heart Sound)-
S1 is made by the closure of the mitral and tricuspid valves (atrioventricular valves), marking the beginning of systole.
S2 (Second Heart Sound)-
S2 is made by the closure of the aortic and pulmonic valves (semilunar valves), marking the end of the systole.
646

You might also read

Related Articles

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

Sort by
Same author

Driving Mechanisms and Future Trends of Migraine and Tension-Type Headache Burden in G20 Countries: A Comparative Analysis Utilizing Decomposition Analysis and Explainable Machine Learning.

Pain and therapy·2026
Same author

Safety threshold quantification for mountainous freeways: a CatBoost-SHAP framework for crash frequency modeling on combined horizontal and vertical curve alignments.

Accident; analysis and prevention·2026
Same author

2D In-Plane Molecular Superlattice Heterojunctions for High-Performance Ambipolar Electronics and Low-Dose X-Ray Sensing.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Safety evaluation of chevron markers and speed reduction markings for mountainous freeway combined alignments: a driving simulator study.

Accident; analysis and prevention·2026
Same author

A Prospective Study of Marsh PK-PD Model and Schnider PK-PD Model During Anesthesia Induction for Obese Patients Undergoing Elective Heart Surgery.

Pharmacology research & perspectives·2026
Same author

Bright Self-Trapped Exciton Emission of CsPbBr<sub>3</sub>@CsPb<sub>2</sub>Br<sub>5</sub> Nanostructures Created by Dissolution and Recrystallization of Hydrophilic Cu:CsPbBr<sub>3</sub>.

Small (Weinheim an der Bergstrasse, Germany)·2026

Related Experiment Video

Updated: Sep 9, 2025

Pulse Wave Velocity Testing in the Baltimore Longitudinal Study of Aging
06:08

Pulse Wave Velocity Testing in the Baltimore Longitudinal Study of Aging

Published on: February 7, 2014

17.3K

[Evaluation method and system for aging effects of autonomic nervous system based on cross-wavelet transform

Juntong Lyu1, Yining Wang1, Wenbin Shi1

  • 1School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|August 31, 2025
PubMed
Summary

This study introduces a new cardiopulmonary coupling (CPC) algorithm to assess autonomic nervous system (ANS) aging. Young individuals show stronger cardio-pulmonary interactions than older adults, validated by wearable devices.

Keywords:
Aging effectsAutonomic nervous systemCardiopulmonary couplingCross-wavelet transformNonstationary signal

More Related Videos

Measuring Cardiac Autonomic Nervous System ANS Activity in Children
09:45

Measuring Cardiac Autonomic Nervous System ANS Activity in Children

Published on: April 29, 2013

21.1K
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

20.0K

Related Experiment Videos

Last Updated: Sep 9, 2025

Pulse Wave Velocity Testing in the Baltimore Longitudinal Study of Aging
06:08

Pulse Wave Velocity Testing in the Baltimore Longitudinal Study of Aging

Published on: February 7, 2014

17.3K
Measuring Cardiac Autonomic Nervous System ANS Activity in Children
09:45

Measuring Cardiac Autonomic Nervous System ANS Activity in Children

Published on: April 29, 2013

21.1K
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

20.0K

Area of Science:

  • Physiology
  • Biomedical Engineering
  • Signal Processing

Context:

  • Traditional heart rate variability (HRV) analysis for autonomic nervous system (ANS) assessment overlooks cardio-pulmonary interactions.
  • Wearable monitoring devices offer potential for continuous, real-world physiological data collection.

Purpose:

  • To develop a novel cardiopulmonary coupling (CPC) algorithm using cross-wavelet transform to quantify cardio-pulmonary interactions.
  • To establish an assessment system for ANS aging effects utilizing wearable ECG and respiratory monitoring.
  • To validate the proposed CPC method's superiority over traditional approaches, especially in nonstationary and low signal-to-noise conditions.

Summary:

  • Simulations confirmed the proposed CPC algorithm's robustness compared to traditional methods.
  • Analysis of young and elderly populations revealed significantly stronger high-frequency band couplings in younger individuals.
  • A CPC assessment system integrated with wearable devices was successfully developed and validated.

Impact:

  • Provides a novel methodological approach for assessing cardio-pulmonary interactions.
  • Offers a system for evaluating the impact of aging on the autonomic nervous system using wearable technology.
  • Enhances understanding of age-related changes in autonomic function through improved physiological coupling analysis.