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

Toward robust automated cardiovascular arrhythmia detection using self-supervised learning and 1-dimensional vision

Mitchell Chatterjee1, Adrian D C Chan2, Majid Komeili3

  • 1School of Computer Science, Carleton University, Ottawa, K1S 5B6, Canada. mitchellchatterjee@cmail.carleton.ca.

Scientific Reports
|March 3, 2026
PubMed
Summary

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

Rectangular Pulse Three-Hour Repetitive Transcranial Magnetic Stimulation Treatment for Major Depressive Disorder: A Pilot Trial.

Neuromodulation : journal of the International Neuromodulation Society·2026
Same author

Two-Step 3D Microphone Array Fusion Algorithm to Enhance Sound Source Location Measurements.

Sensors (Basel, Switzerland)·2026
Same author

Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review.

European respiratory review : an official journal of the European Respiratory Society·2026
Same author

Deep Learning Analysis of Prenatal Ultrasound for Identification of Ventriculomegaly.

Ultrasound in medicine & biology·2026
Same author

Sound and Vibration Exposure During Fixed-Wing Aircraft Neonatal Patient Transport.

Air medical journal·2026
Same author

Automated mean linear intercept measurement: quantifying bias and parameter sensitivity in lung morphometry.

Physiological measurement·2025
This summary is machine-generated.

Self-supervised learning with Masked Patch Modelling (MPM) enhances arrhythmia detection from electrocardiogram (ECG) data. PatchECG, a novel 1D Transformer model, achieves state-of-the-art results efficiently, improving automated cardiovascular disease diagnosis.

Area of Science:

  • Artificial Intelligence
  • Biomedical Engineering
  • Cardiology

Background:

  • Cardiovascular diseases are a leading global cause of mortality.
  • Electrocardiogram (ECG) monitoring is increasingly accessible for passive arrhythmia detection.
  • Challenges in ECG analysis include class imbalance and noise, hindering traditional machine learning models.

Purpose of the Study:

  • To leverage self-supervised learning on large-scale unlabeled ECG data for improved arrhythmia detection.
  • To introduce and evaluate PatchECG, a novel 1D Transformer model, for ECG analysis.
  • To enhance model performance, efficiency, and robustness against common ECG data issues.

Main Methods:

  • Utilized Masked Patch Modelling (MPM) for self-supervised pre-training on 8.2 million unlabeled ECGs.
Keywords:
Cardiac arrhythmiaDeep learningElectrocardiographyMedical diagnosisMedical signal classificationSelf-supervised learning

Related Experiment Videos

  • Developed PatchECG, a 1D Transformer architecture, for various ECG classification tasks.
  • Fine-tuned PatchECG on standard datasets (e.g., PTB-XL) and a large, high-quality multi-label dataset.

    • Main Results:

    • PatchECG achieved state-of-the-art performance on benchmark datasets, setting new records on a large multi-label dataset.
    • The model demonstrated a 5x increase in computational efficiency and a 14x increase in model capacity compared to existing methods.
    • PatchECG outperformed a state-of-the-art 2D vision Transformer (HeartBEiT) and showed a 2% improvement in handling data challenges like class imbalance and noise.

    Conclusions:

    • Self-supervised learning, particularly with PatchECG, significantly advances automated arrhythmia detection.
    • The PatchECG model offers a computationally efficient and highly effective solution for analyzing ECG data.
    • This approach holds substantial potential for improving cardiovascular disease diagnosis and patient outcomes through automated monitoring.