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

Multiple cardiac arrhythmia recognition using adaptive wavelet network.

Chia-Hung Lin1, Pei-Jarn Chen, Yung-Fu Chen

  • 1Department of Electrical Engineering, Kao-Yuan Institute of Technology, Kaohsiung 821, Taiwan; Institute of Biomedical Engineering, National Cheng-Kung University, Tainan 701, Taiwan.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 7, 2007
PubMed
Summary
This summary is machine-generated.

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

Upper Limb Tremors Classification for Parkinson's Disease Using W-Band (76-81 GHz) Doppler Millimeter-Wave Sensing and Deep-Learning-Based Classifier.

Sensors (Basel, Switzerland)·2026
Same author

Early nutritional consultation after emergency department MNA-SF assessment in hospitalized malnourished older adults.

Nutrition (Burbank, Los Angeles County, Calif.)·2026
Same author

Prognostic implications of single antiplatelet therapy in individuals developing diabetic foot disease with concurrent peripheral arterial disease.

Annals of medicine·2026
Same author

Zebrafish Model Reveals Early Electrocardiographic and Molecular Signatures of Doxorubicin-Induced Cardiotoxicity.

Cardiovascular toxicology·2026
Same author

Association Between Depression Subtypes and Interstitial Cystitis/Bladder Pain Syndrome.

International urogynecology journal·2026
Same author

Sequential α-Aminoalkyl Radical and Oxygen-Centered Radical Reactions of Vinyl Bromide-Tethered Tricyclic Lactones.

Chemistry, an Asian journal·2026

This study introduces an adaptive wavelet network (AWN) for electrocardiogram (ECG) arrhythmia detection. The method accurately identifies cardiac arrhythmias by analyzing ECG heartbeat patterns using wavelet transforms and probabilistic neural networks.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Electrocardiogram (ECG) analysis is crucial for diagnosing cardiac conditions.
  • Accurate identification of cardiac arrhythmias requires robust feature extraction and classification methods.
  • Existing methods may face challenges in dynamic environments and require manual parameter tuning.

Purpose of the Study:

  • To propose a novel Adaptive Wavelet Network (AWN) for automated ECG heartbeat pattern recognition and cardiac arrhythmia identification.
  • To develop a two-subnetwork architecture combining wavelet analysis and probabilistic neural networks for enhanced discrimination.
  • To demonstrate the AWN's capability for real-time adaptation and parameter tuning in dynamic environments.

Main Methods:

Related Experiment Videos

  • Feature extraction from ECG QRS complexes using Morlet wavelets.
  • Classification of heartbeat patterns using a Probabilistic Neural Network (PNN).
  • Implementation of an AWN with automatic target adjustment and parameter tuning for dynamic environments.

    • Main Results:

    • The proposed AWN method achieved efficient ECG beat recognition and cardiac arrhythmia identification.
    • Experimental validation using the MIT-BIH arrhythmia database demonstrated the method's effectiveness.
    • The two-subnetwork architecture successfully extracted and analyzed meaningful features for discrimination.

    Conclusions:

    • The AWN provides an efficient and adaptive approach for ECG arrhythmia detection.
    • The combination of wavelet features and PNN offers a powerful tool for analyzing complex cardiac signals.
    • The method shows promise for clinical applications requiring dynamic and automated analysis of ECG data.