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

Pulse rhythm01:30

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac muscle...
Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

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 heart...
Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
Dysrhythmias IV: Characteristics of Bradyarrhythmias01:18

Dysrhythmias IV: Characteristics of Bradyarrhythmias

Bradyarrhythmias are cardiac rhythm disorders characterized by a slower-than-normal heart rate, typically defined as fewer than 60 beats per minute. Some of which are discussed here:Sinus BradycardiaSinus bradycardia presents a heart rate lower than 60 beats per minute, with a regular rhythm originating from the SA node. The ECG typically shows normal P waves preceding each QRS complex, a normal PR interval (0.12 to 0.20 seconds), and a normal QRS duration (0.06 to 0.10 seconds).First-Degree AV...
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
Dysrhythmias VI: Management of Dysrhythmias01:25

Dysrhythmias VI: Management of Dysrhythmias

Dysrhythmia management involves a multifaceted approach, incorporating pharmacological treatments, medical procedures, surgical interventions, lifestyle modifications, and patient education.Pharmacological ManagementAntiarrhythmic Drugs:Class I (Sodium Channel Blockers): This class includes quinidine and procainamide, which reduce the speed of impulse conduction in the heart, stabilize the cardiac membrane, and control arrhythmias. Quinidine and procainamide are Class IA agents that prolong the...

You might also read

Related Articles

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

Sort by
Same author

[ATM/H2AX and repair of sperm-DNA damage during cryopreservation].

Zhonghua nan ke xue = National journal of andrology·2011
Same author

Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model.

Accident; analysis and prevention·2011
Same author

Photothermally enhanced photodynamic therapy delivered by nano-graphene oxide.

ACS nano·2011
Same author

[Characteristics of soil respiration in Phyllostachys edulis forest in Wanmulin Natural Reserve and related affecting factors].

Ying yong sheng tai xue bao = The journal of applied ecology·2011
Same author

Quality changes in sea urchin (Strongylocentrotus nudus) during storage in artificial seawater saturated with oxygen, nitrogen and air.

Journal of the science of food and agriculture·2011
Same author

Global effect of an RNA polymerase β-subunit mutation on gene expression in the radiation-resistant bacterium Deinococcus radiodurans.

Science China. Life sciences·2011
Same journal

Semi-supervised YOLO-DEP for high-resolution X-ray component localization and counting.

Journal of X-ray science and technology·2026
Same journal

Attention based multi-scale edge-aware segmentation and convolutional transformer framework for automated glaucoma detection from fundus images.

Journal of X-ray science and technology·2026
Same journal

Improving the robustness of radiomic features to patient size variations in CBCT imaging for radiotherapy.

Journal of X-ray science and technology·2026
Same journal

DH-OOD: A decoupled hybrid framework for robust skin lesion classification via semantic-structural fusion.

Journal of X-ray science and technology·2026
Same journal

Development and evaluation of deep learning models for automatic coronary stenosis segmentation in X-ray angiography.

Journal of X-ray science and technology·2026
Same journal

Projection-domain reconstruction of patient-specific panoramic images from CBCT projection data.

Journal of X-ray science and technology·2026
See all related articles

Related Experiment Video

Updated: May 19, 2026

Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

12.5K

De-noising surface electromyograms using an adaptive wavelet approach.

Wei Wei1, Jie Hong1, Chao Wang1

  • 1Department of Mechanical Engineering, Anhui University of Technology, Maanshan, Anhui, China.

Journal of X-Ray Science and Technology
|February 7, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel de-noising function for surface Electromyogram (sEMG) signals. The new method effectively removes noise, outperforming traditional techniques for clearer signal analysis.

Keywords:
Surface electromyogram signalde-noisingsignal-to-noise ratiothreshold function

More Related Videos

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

14.6K
Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

419

Related Experiment Videos

Last Updated: May 19, 2026

Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

12.5K
Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
08:08

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

14.6K
Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

419

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Neuroscience

Background:

  • Surface Electromyogram (sEMG) signals are prone to noise during acquisition.
  • Conventional wavelet threshold de-noising methods suffer from the Pseudo-Gibbs phenomenon, leading to inevitable noise.
  • Effective noise reduction is crucial for accurate sEMG signal analysis.

Purpose of the Study:

  • To investigate the feasibility of a new de-noising function for sEMG signals.
  • To leverage the adaptive threshold from the Brige-Massart algorithm for improved noise removal.
  • To evaluate the performance of the proposed de-noising function.

Main Methods:

  • Development of a new de-noising function incorporating an adaptive threshold.
  • Utilizing the Brige-Massart algorithm's adaptive threshold principle.
  • Simulation-based testing and comparison with conventional de-noising methods.

Main Results:

  • The new de-noising function effectively removes noise from sEMG signals.
  • The proposed method demonstrates superior de-noising performance compared to conventional techniques.
  • Improved signal quality facilitates subsequent signal analysis.

Conclusions:

  • The developed de-noising function is effective in minimizing noise in sEMG signals.
  • The adaptive threshold approach offers significant advantages over traditional de-noising methods.
  • This advancement enables more reliable and accurate analysis of sEMG data.