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

Regulation of Stroke Volume01:27

Regulation of Stroke Volume

3.4K
The regulation of stroke volume, which is the amount of blood the heart pumps out during each heartbeat, is critical for maintaining a healthy circulatory system. Stroke volume is influenced by three main factors: preload, contractility, and afterload.
Preload refers to the degree of stretch on the heart before it contracts. It's analogous to the stretching of a rubber band; the more it's stretched, the more forcefully it snaps back. This concept is encapsulated in the Frank-Starling law of the...
3.4K
Cardiac Output II: Effect of Stroke Volume on Cardiac Output01:22

Cardiac Output II: Effect of Stroke Volume on Cardiac Output

1.3K
Cardiac output (CO), the amount of blood the heart pumps per minute, is a parameter in cardiovascular physiology determined by stroke volume and heart rate. Stroke volume, the amount of blood pushed from one of the ventricles per heartbeat, is influenced by preload, afterload, and contractility.
Preload
Preload refers to the initial elongation of the cardiac myocytes before contraction and is related to the volume of blood filling the heart at the end of diastole, or end-diastolic volume. The...
1.3K
Cardiac Output and Stroke Volume01:11

Cardiac Output and Stroke Volume

3.1K
Cardiac output (CO) is an integral aspect of human physiology, reflecting the heart's efficiency and responsiveness to the body's needs. It represents the volume of blood that the left or right ventricle ejects into the aorta or pulmonary trunk each minute. The CO is calculated by multiplying the heart rate (HR)—the number of heartbeats per minute—by the stroke volume (SV)—the amount of blood pumped out with each heartbeat.
In an average resting adult male, the typical cardiac...
3.1K

You might also read

Related Articles

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

Sort by
Same author

Perioperative advanced haemodynamic monitoring of patients undergoing multivisceral debulking surgery: an observational pilot study.

Intensive care medicine experimental·2023
Same author

Detection of a Stroke Volume Decrease by Machine-Learning Algorithms Based on Thoracic Bioimpedance in Experimental Hypovolaemia.

Sensors (Basel, Switzerland)·2022
Same author

Surrogate based continuous noninvasive blood pressure measurement.

Biomedizinische Technik. Biomedical engineering·2021
Same author

CHARGE syndrome: genetic aspects and dental challenges, a review and case presentation.

Head & face medicine·2020
Same author

Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVETand Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram.

Sensors (Basel, Switzerland)·2020
Same author

Balanced Adjustable Mirrored Current Source with Common Mode Feedback and Output Measurement for Bioimpedance Applications.

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

Related Experiment Video

Updated: Aug 23, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

950

GRU Neural Network Improved Bioimpedance Based Stroke Volume Estimation during Ergometry Stress Test.

Mike Urban1,2, Michael Klum1, Alexandru-Gabriel Pielmus1

  • 1Department of Electronics and Medical Signal Processing, Technische Universität Berlin, Einsteinufer 17, 10587 Berlin, Germany.

Sensors (Basel, Switzerland)
|October 27, 2022
PubMed
Summary

This study introduces a new gated recurrent unit (GRU) neural network method to improve hemodynamic measurements during exercise stress tests. The GRU network enhances accuracy by reducing signal artifacts, aiding early cardiovascular disease diagnosis.

Keywords:
ECGGRU neural networkICGbioimpedancecardiac outputcardiovascular diseaseshemodynamic parametersimpedance cardiographysignal processingstroke volume

More Related Videos

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
11:06

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

10.5K
Evaluation of Fluid Overload by Bioelectrical Impedance Vectorial Analysis
07:17

Evaluation of Fluid Overload by Bioelectrical Impedance Vectorial Analysis

Published on: August 17, 2022

2.6K

Related Experiment Videos

Last Updated: Aug 23, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

950
A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
11:06

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

10.5K
Evaluation of Fluid Overload by Bioelectrical Impedance Vectorial Analysis
07:17

Evaluation of Fluid Overload by Bioelectrical Impedance Vectorial Analysis

Published on: August 17, 2022

2.6K

Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Artificial Intelligence

Background:

  • Cardiovascular diseases (CVDs) are a leading cause of non-communicable diseases, necessitating early diagnosis for effective treatment.
  • Exercise stress tests are common for CVD identification, but hemodynamic monitoring accuracy can be limited.
  • Thoracic electrical bioimpedance offers beat-to-beat stroke volume calculation but is sensitive to motion artifacts.

Purpose of the Study:

  • To develop an improved method for measuring bioimpedance signals during exercise stress tests.
  • To reduce signal artifacts and accurately calculate hemodynamic parameters.
  • To enhance the diagnostic capabilities for cardiovascular diseases.

Main Methods:

  • A novel approach utilizing a gated recurrent unit (GRU) neural network combined with ECG signals.
  • Investigating hemodynamic status and parameters during exercise.
  • Comparing the GRU network's performance against ensemble averaging, adaptive filters, and shallow neural networks.

Main Results:

  • The GRU neural network demonstrated superior performance in handling single artifact events compared to shallow neural networks.
  • Achieved a mean error of -0.0244 and mean square error of 0.0181 for normalized stroke volume.
  • The GRU network proved more effective than other algorithms in processing time-correlated data during exercise stress tests.

Conclusions:

  • The proposed GRU neural network method significantly improves the accuracy of bioimpedance measurements during exercise stress tests.
  • This technique offers a promising advancement for non-invasive hemodynamic monitoring and cardiovascular disease diagnostics.
  • The GRU network's ability to reduce artifacts enhances the reliability of stroke volume calculations.