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

ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

14.2K
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....
14.2K
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

425
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
425
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

12.7K
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...
12.7K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

857
Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...
857
Block Diagram Reduction01:22

Block Diagram Reduction

567
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
567

You might also read

Related Articles

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

Sort by
Same author

Comparison of Visual Outcomes Between Bilateral Implantation of Multifocal Intraocular Lens and Mix-and-Match Strategy.

Journal of refractive surgery (Thorofare, N.J. : 1995)·2026
Same author

Prevalence of frailty in hemodialysis patients and its impact on long-term survival in elderly hemodialysis patients.

Frontiers in medicine·2026
Same author

Accuracy of 8 Modern Intraocular Lens Power Calculation Formulas in Asian Eyes With Axial Length ≥ 32.00 mm.

American journal of ophthalmology·2026
Same author

High protective efficacy of a recombinant Fer2 vaccine against Dermacentor marginatus infestations.

Experimental & applied acarology·2026
Same author

Swept-source OCTA assessment of iris vasculature as a biomarker for myopic macular degeneration severity.

Advances in ophthalmology practice and research·2026
Same author

Smart Nanodelivery for Eye-Brain Disorders: Synergistic CCL2 Neutralization and MMP9 Silencing Reverse Anxiety in High Myopia.

Small (Weinheim an der Bergstrasse, Germany)·2026

Related Experiment Video

Updated: Feb 8, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K

A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse

Yunfei Cheng1, Yalan Ye2, Mengshu Hou3

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China. yunfeicheng@hotmail.com.

Sensors (Basel, Switzerland)
|June 26, 2018
PubMed
Summary

This study introduces a new algorithm for wearable electrocardiogram (ECG) monitoring that efficiently recovers non-sparse ECG signals, crucial for low-power wireless body area networks (WBANs). The method ensures accurate, fast, and robust long-term patient monitoring.

Keywords:
alternating direction method of multipliers (ADMM)block sparse Bayesian learning (BSBL)compressed sensing (CS)electrocardiogram (ECG)wireless body area networks (WBAN)

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
10:03

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

Published on: July 22, 2022

5.0K

Related Experiment Videos

Last Updated: Feb 8, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
10:03

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

Published on: July 22, 2022

5.0K

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Telemedicine

Background:

  • Wearable electrocardiogram (ECG) monitoring via wireless body area networks (WBANs) is vital for patient-centric telecardiology.
  • Limited sensor power consumption is essential for long-term monitoring effectiveness.
  • Traditional compressed sensing (CS) struggles with non-sparse ECG signals common in WBANs.

Purpose of the Study:

  • To develop a fast and robust algorithm for recovering non-sparse ECG signals in wearable telemonitoring.
  • To address the limitations of existing CS recovery methods for ECG data.

Main Methods:

  • Proposed a novel algorithm combining the alternating direction method of multipliers (ADMM) to accelerate the block sparse Bayesian learning (BSBL) framework.
  • Validated the algorithm using the MIT-BIH Arrhythmia and Long-Term ECG databases, plus practical wearable ECG data.
  • Focused on direct time-domain signal recovery without requiring a dictionary matrix.

Main Results:

  • The algorithm successfully recovered ECG signals with satisfactory accuracy directly in the time domain.
  • The ADMM acceleration resulted in a significantly fast processing speed.
  • Demonstrated robustness across diverse ECG datasets, including those from a practical wearable system.

Conclusions:

  • The proposed algorithm offers a promising solution for efficient and accurate ECG signal recovery in wearable telemonitoring systems.
  • Its speed and robustness make it suitable for low-power WBAN applications.
  • Facilitates next-generation patient-centric telecardiology solutions.