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 Video

Updated: Nov 4, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

733

Multiscale Residual Network Based on Channel Spatial Attention Mechanism for Multilabel ECG Classification.

Shuhong Wang1,2, Runchuan Li1,2, Xu Wang1,2

  • 1School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China.

Journal of Healthcare Engineering
|May 31, 2021
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

Combining Rhythm Information between Heartbeats and BiLSTM-Treg Algorithm for Intelligent Beat Classification of Arrhythmia.

Journal of healthcare engineering·2021
Same author

Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features.

Journal of healthcare engineering·2021
Same author

An Intelligent Heartbeat Classification System Based on Attributable Features with AdaBoost+Random Forest Algorithm.

Journal of healthcare engineering·2021
Same author

Heartbeat Classification Based on Multifeature Combination and Stacking-DWKNN Algorithm.

Journal of healthcare engineering·2021
Same author

Efficient Deep Learning Architecture for Detection and Recognition of Thyroid Nodules.

Computational intelligence and neuroscience·2020
Same author

Expression and clinical diagnostic value of miR-383 in patients with severe preeclampsia.

Cellular and molecular biology (Noisy-le-Grand, France)·2020

This study introduces CSA-MResNet, a novel deep learning model for accurate multi-label electrocardiogram (ECG) classification. The model enhances early cardiovascular disease diagnosis by identifying multiple conditions from a single ECG record.

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Electrocardiogram (ECG) analysis is crucial for cardiovascular disease diagnosis.
  • Current ECG classification models primarily address single-label problems, limiting clinical utility.
  • Real-world ECGs often exhibit multiple concurrent pathologies, necessitating multi-label classification.

Purpose of the Study:

  • To develop an advanced deep learning model for accurate multi-label ECG classification.
  • To address the limitation of single-label classification in clinical ECG analysis.
  • To improve early detection and auxiliary diagnosis of cardiovascular diseases.

Main Methods:

  • Proposed a novel multi-scale residual deep neural network (CSA-MResNet) incorporating a channel-spatial attention mechanism.

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

592

Related Experiment Videos

Last Updated: Nov 4, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

733
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

592
  • Integrated residual networks in a multi-scale approach to capture diverse ECG data features.
  • Employed a channel-spatial attention mechanism to focus on critical ECG segments and channels.

    • Main Results:

    • Achieved an average F1 score of 88.2% for multi-label classification of 9 conditions on the CCDD dataset.
    • Demonstrated a 1.7% increase in F1 score compared to benchmark models for multi-label ECG classification.
    • Attained an average F1 score of 85.8% on the HF-challenge dataset, showing robust performance.

    Conclusions:

    • CSA-MResNet offers a feasible and effective method for multi-label ECG classification.
    • The model aids cardiologists in rapid, early-stage ECG screening.
    • Demonstrated generalization performance across different ECG databases.