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

Detection of Black Holes01:10

Detection of Black Holes

2.3K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.3K
Divergence and Curl of Magnetic Field01:26

Divergence and Curl of Magnetic Field

3.3K
The magnetic field due to a volume current distribution given by the Biot–Savart Law can be expressed as follows:
3.3K
Propagation of Waves01:07

Propagation of Waves

2.5K
When a wave propagates from one medium to another, part of it may get reflected in the first medium, and part of it may get transmitted to the second medium. In such a case, the interface of the two mediums can be considered as a boundary that is neither fixed nor free.
Consider a scenario where a wave propagates from a string of low linear mass density to a string of high linear mass density. In such a case, the reflected wave is out of phase with respect to the incident wave, however the...
2.5K
Classification of Signals01:30

Classification of Signals

1.0K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.0K
Wave Parameters01:10

Wave Parameters

8.4K
The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
8.4K
Perception of Sound Waves01:01

Perception of Sound Waves

4.8K
The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same...
4.8K

You might also read

Related Articles

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

Sort by
Same author

Influence of conduction heterogeneities on transient spatiotemporal chaos in cardiac excitable media.

Physical review. E·2024
Same author

Chaos control in cardiac dynamics: terminating chaotic states with local minima pacing.

Frontiers in network physiology·2024
Same author

Optimising low-energy defibrillation in 2D cardiac tissue with a genetic algorithm.

Frontiers in network physiology·2023
Same author

The role of pulse timing in cardiac defibrillation.

Frontiers in network physiology·2023
Same author

Taming cardiac arrhythmias: Terminating spiral wave chaos by adaptive deceleration pacing.

Chaos (Woodbury, N.Y.)·2023
Same author

Non-monotonous dose response function of the termination of spiral wave chaos.

Scientific reports·2022

Related Experiment Video

Updated: Oct 18, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.4K

Detecting spiral wave tips using deep learning.

Henning Lilienkamp1,2, Thomas Lilienkamp3,4

  • 1GFZ Helmholtz Centre Potsdam, Seismic Hazard and Risk Dynamics, Potsdam, 14467, Germany.

Scientific Reports
|October 6, 2021
PubMed
Summary

Deep neural networks (UNet) reliably detect spiral wave tips in cardiac arrhythmias, even with noise. This AI approach generalizes across models, offering future potential for experimental use.

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K
Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

11.7K

Related Experiment Videos

Last Updated: Oct 18, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.4K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K
Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

11.7K

Area of Science:

  • Computational Biology
  • Cardiac Electrophysiology
  • Artificial Intelligence in Medicine

Background:

  • Cardiac arrhythmias, such as ventricular fibrillation, involve complex electrical activity driven by spiral or scroll waves.
  • Wave tips (2D) and filaments (3D) organize these chaotic dynamics and are crucial for understanding and controlling arrhythmias.
  • Current methods for detecting wave tips are often sensitive to noise and lack generalizability across different cardiac models.

Purpose of the Study:

  • To develop and validate a robust method for detecting spiral wave tips in cardiac electrical activity.
  • To assess the performance of deep neural networks (UNet) in identifying wave tips under noisy conditions.
  • To evaluate the generalizability of a trained UNet model across diverse numerical cardiac cell models.

Main Methods:

  • Application of a deep neural network, specifically a UNet architecture, for automated spiral wave tip detection.
  • Training the UNet model using data from various numerical cardiac cell models to enhance its adaptability.
  • Testing the model's robustness against varying levels of noise and its performance on previously unseen cardiac models.

Main Results:

  • The UNet model demonstrated significant robustness in detecting spiral wave tips even in the presence of intermediate noise levels.
  • The model successfully generalized its detection capabilities to unknown cardiac cell models after being trained on a diverse pool of models.
  • This suggests the UNet learns a generalizable representation of spiral wave tip dynamics.

Conclusions:

  • Deep neural networks (UNet) offer a powerful and noise-resilient tool for detecting spiral wave tips in cardiac electrophysiology.
  • The demonstrated generalizability of the UNet model across different cardiac models paves the way for its application in experimental settings.
  • This AI-driven approach holds promise for advancing the study and potential therapeutic interventions for life-threatening cardiac arrhythmias.