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

Neural-network-based adaptive matched filtering for QRS detection.

Q Xue1, Y H Hu, W J Tompkins

  • 1Department of Electrical and Computer Engineering, University of Wisconsin, Madison 53706.

IEEE Transactions on Bio-Medical Engineering
|April 1, 1992
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

Draft whole-genome and mitochondrial genome assemblies of <i>Steinernema tarimense</i> and <i>Heterorhabditis</i> sp. XJ-55.

Journal of helminthology·2026
Same author

[A case of invasive fungal rhinosinusitis complicated with intracranial Aspergillosis presenting with blindness as the main complaint].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery·2026
Same author

[Define benchmarks for low-risk sleeve gastrectomy: based on the greater China metabolic and bariatric surgery database].

Zhonghua wai ke za zhi [Chinese journal of surgery]·2026
Same author

[Research progress on precise lifestyle intervention for obesity based on genetic background].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2025
Same author

[Application of pouch technique in the reconstruction of severe alveolar bone defects: a case report].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology·2025
Same author

[Application of pouch technique in the reconstruction of severe alveolar bone defects].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology·2025
Same journal

Enhancing Volumetric Imaging in Linear-Array Photoacoustic Tomography: multiview fusion with deep learning.

IEEE transactions on bio-medical engineering·2026
Same journal

Robust Rule-based Heuristic Assistance Strategy for a Semi-Active Shoulder Exoskeleton Used in Overhead Work.

IEEE transactions on bio-medical engineering·2026
Same journal

Highly Accelerated 1-mm Isotropic 3D Chemical Exchange Saturation Transfer MRI Using Wave-Co-CAIPI at 5 Tesla.

IEEE transactions on bio-medical engineering·2026
Same journal

Systematic Evaluation of Hip Exoskeleton Assistance Parameters for Enhancing Gait Stability During Ground Slip Perturbations.

IEEE transactions on bio-medical engineering·2026
Same journal

SleepConFormer: A Single-Channel EEG Framework for Sleep Staging and Consciousness Assessment in Patients with Disorders of Consciousness.

IEEE transactions on bio-medical engineering·2026
Same journal

Modeling Partial and Total Support of Left Ventricular Assist Device for Discrete Hemodynamic Control Framework.

IEEE transactions on bio-medical engineering·2026
See all related articles

This study introduces an artificial neural network (ANN) for adaptive QRS detection in ECG signals. The novel ANN filter improves QRS detection accuracy, especially in noisy data.

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence in Medicine

Background:

  • Electrocardiogram (ECG) signals contain complex, nonlinear, and nonstationary noise.
  • Accurate QRS complex detection is crucial for diagnosing cardiac arrhythmias.
  • Traditional filtering methods struggle with time-varying, nonlinear noise in ECG.

Purpose of the Study:

  • To develop an advanced QRS detection algorithm using artificial neural networks (ANNs).
  • To improve QRS detection accuracy in noisy ECG signals.
  • To create an adaptive matched filter that customizes to individual subjects.

Main Methods:

  • An ANN adaptive whitening filter models nonlinear, nonstationary low-frequency ECG components.
  • A linear matched filter processes the residual signal for QRS complex detection.

Related Experiment Videos

  • An adaptive algorithm updates the matched filter template from detected QRS complexes.

    • Main Results:

    • The novel ANN-based approach achieved a 99.5% detection rate on a noisy ECG dataset.
    • This performance surpasses linear adaptive whitening (97.5%) and bandpass filtering (96.5%).
    • The ANN filter effectively removes time-varying, nonlinear noise.

    Conclusions:

    • The developed ANN-based adaptive matched filtering algorithm offers superior QRS detection performance.
    • This method is highly effective for analyzing noisy ECG signals.
    • The adaptive template customization enhances subject-specific QRS detection.