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

Classification of Signals01:30

Classification of Signals

623
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...
623
Classification of Systems-I01:26

Classification of Systems-I

242
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
242
Classification of Systems-II01:31

Classification of Systems-II

199
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
199

You might also read

Related Articles

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

Sort by
Same author

MnL-TWA: Manifold Learning Approach for T-Wave Alternans Detection in Ambulatory Environments.

Biomedical engineering and computational biology·2026
Same author

Ablation of visually identified spatiotemporal dispersion plus pulmonary vein isolation in persistent atrial fibrillation.

European heart journal open·2026
Same author

DeepTWA-TM: Deep Learning T-Wave Alternans Detection in Ambulatory ECG via Time Analysis.

IEEE journal of biomedical and health informatics·2025
Same author

A Deep and Interpretable Learning Approach for Long-Term ECG Clinical Noise Classification.

IEEE transactions on bio-medical engineering·2024
Same author

Electroporation saves the day again: Pulsed-field ablation for phrenic nerve-sparing in right atrial tachycardia.

Journal of cardiovascular electrophysiology·2024
Same author

Machine learning based detection of T-wave alternans in real ambulatory conditions.

Computer methods and programs in biomedicine·2024
Same journal

Novel Parent Survey Measures Sensory Behaviors Incorporating Sensory Modality and Stimulus Intensity.

Heliyon·2026
Same journal

Expression of concern: "SQSTM1/p62 promotes the progression of gastric cancer through epithelial-mesenchymal transition" [Heliyon 10 (2024) e24409].

Heliyon·2026
Same journal

Expression of concern: "TL1A promotes metastasis and EMT process of colorectal cancer" [Heliyon 10 (2024) e24392].

Heliyon·2026
Same journal

Expression of concern: "Factors affecting timing of surgery following neoadjuvant chemoradiation for esophageal cancer" [Heliyon 9 (2023) e23212].

Heliyon·2026
Same journal

Expression of concern: "On stratified single-valued soft topogenous structures" [Heliyon 10 (2024) e27926].

Heliyon·2026
Same journal

Expression of concern: "Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN" [Heliyon 10 (2024) e27198].

Heliyon·2026
See all related articles

Related Experiment Video

Updated: Aug 12, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

Machine Learning approach for TWA detection relying on ensemble data design.

Miriam Gutiérrez Fernández-Calvillo1, Rebeca Goya-Esteban2, Fernando Cruz-Roldán1

  • 1Department of Teoría de la Señal y Comunicaciones, Universidad de Alcalá, Madrid, Spain.

Heliyon
|January 26, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning algorithms effectively detect T-wave alternans (TWA), a marker for sudden cardiac death risk. This study introduces a novel machine learning approach and a benchmarking system, outperforming traditional methods.

Keywords:
Cross Validation (CV)Electrocardiogram (ECG)Machine Learning (ML)Modified Moving Average Method (MMA)RepolarizationSpectral Method (SM)Time Method (TM)T–Wave Alternans (TWA)

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

833
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Related Experiment Videos

Last Updated: Aug 12, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

833
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Machine Learning in Healthcare

Background:

  • T-wave alternans (TWA) is a crucial electrocardiogram (ECG) marker for sudden cardiac death risk stratification.
  • Current TWA detection methods lack a gold standard for benchmarking, hindering clinical application and development.
  • A novel machine learning (ML) approach and experimental setup are proposed for TWA detection and method evaluation.

Purpose of the Study:

  • To develop and validate a novel machine learning-based approach for T-wave alternans detection.
  • To establish a robust experimental setup for benchmarking TWA detection methods using open-source databases and real ECG signals.
  • To compare the performance of various ML algorithms against established TWA detection techniques.

Main Methods:

  • A realistic database was created using open-source ECG signals with simulated TWA episodes, avoiding intra-patient overfitting and class imbalance.
  • Features were extracted using the Spectral Method (SM), Modified Moving Average Method (MMA), and Time Domain Method (TM).
  • Machine learning algorithms including K Nearest Neighbor, Decision Trees, Random Forest, Support Vector Machine, and Multi-Layer Perceptron were employed.

Main Results:

  • Machine learning algorithms demonstrated improved performance over the Spectral Method (SM), a traditional gold standard.
  • Decision Trees exhibited the best overall performance among the tested ML algorithms.
  • While precision slightly decreased, accuracy, recall, and F1 score showed significant improvements compared to the SM.

Conclusions:

  • The developed machine learning approach, utilizing a realistic database, significantly enhances TWA detection capabilities.
  • ML algorithms generally outperformed the Spectral Method, offering a more sensitive detection of TWA.
  • This study provides a validated framework for TWA detection and benchmarking, paving the way for improved cardiac risk assessment.