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 Experiment Videos

An input-delay neural-network-based approach for piecewise ECG signal compression.

Amitava Chatterjee1, Amine Nait-Ali, Patrick Siarry

  • 1Université de Paris 12, 700047 Kolkata West Bengal, India. cha_ami@yahoo.co.in

IEEE Transactions on Bio-Medical Engineering
|May 13, 2005
PubMed
Summary

We developed a new algorithm using an input delay neural network (IDNN) for compressing electrocardiogram (ECG) signals. This method offers superior compression efficiency and signal reconstruction compared to existing techniques.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Moving beyond book values: Using machine learning to improve the accuracy of manure nutrient concentration predictions.

Journal of environmental quality·2026
Same author

Topology constrained nonnegative matrix factorization for time varying omic expression.

Scientific reports·2026
Same author

UaBMA-OLPP: A Novel Manifold-Inspired Technique for sEMG-Based Hand Movement and Object Grasp Recognition.

IEEE journal of biomedical and health informatics·2025
Same author

A Review on Surgical Robotic Platforms Including RMIS, AI-Based CAS Systems, Challenges, and Future Directions.

The international journal of medical robotics + computer assisted surgery : MRCAS·2025
Same author

A new particle swarm optimization-enhanced deep neural network for automatic ECG arrhythmias classification.

Computer methods in biomechanics and biomedical engineering·2025
Same author

Enhancing random forest model prediction of gas holdup in internal draft airlift loop contactors with genetic algorithms tuning and interpretability.

Scientific reports·2025

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Electrocardiogram (ECG) signals are crucial for diagnosing cardiac conditions.
  • Efficient compression of ECG data is vital for storage, transmission, and analysis.
  • Existing compression methods face limitations in balancing compression ratios and signal fidelity.

Purpose of the Study:

  • To introduce a novel time series prediction algorithm for ECG signal compression.
  • To evaluate the performance of the proposed algorithm against established compression techniques.
  • To demonstrate the effectiveness of input delay neural networks (IDNNs) in ECG data compression.

Main Methods:

  • Development of an input delay neural network (IDNN) based algorithm.

Related Experiment Videos

  • Application of the algorithm for time series prediction and subsequent ECG signal compression.
  • Comparative analysis of the IDNN algorithm with other popular compression techniques.

    • Main Results:

    • The proposed IDNN algorithm achieved significant compression efficiency for ECG signals.
    • Reconstruction capability of the compressed ECG signals was successfully maintained.
    • The IDNN-based method demonstrated superior performance compared to existing popular techniques.

    Conclusions:

    • The input delay neural network (IDNN) offers a promising approach for efficient ECG signal compression.
    • The algorithm provides a favorable trade-off between compression ratio and signal reconstruction quality.
    • This method has the potential to improve the management and analysis of large-scale ECG datasets.