ArticleiScience2020
Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed, 185 citations in OpenAlex.
- Real-time ECG-based detection of cardiovascular diseases using balanced and interpretable machine learning approaches.Physical and engineering sciences in medicine · 2026Article
- Article
- Assessing the Impact of Downsampled ECGs and Alternative Loss Functions in Multi-Label Classification of 12-Lead ECGs.Cardiovascular engineering and technology · 2025Article
- QRS-centric beat-wise atrial fibrillation detection in ECG signals using deep neural networks.Computers in biology and medicine · 2025Article
- Causal ECGNet: leveraging causal inference for robust ECG classification in cardiac disorders.Frontiers in physiology · 2025Article
- ECG classification via integration of adaptive beat segmentation and relative heart rate with deep learning networks.Computers in biology and medicine · 2024Article
- A coordinated adaptive multiscale enhanced spatio-temporal fusion network for multi-lead electrocardiogram arrhythmia detection.Scientific reports · 2024Article
- Beat-by-Beat Estimation of Hemodynamic Parameters in Left Ventricle Based on Phonocardiogram and Photoplethysmography Signals Using a Deep Learning Model: Preliminary Study.Bioengineering (Basel, Switzerland) · 2024Article
- Age prediction from 12-lead electrocardiograms using deep learning: a comparison of four models on a contemporary, freely available dataset.Physiological measurement · 2024Article
- Efficient Edge-AI Models for Robust ECG Abnormality Detection on Resource-Constrained Hardware.Journal of cardiovascular translational research · 2024Article
- Visualized Lead Selection for Arrhythmia Classification Based on a Lead Activation Heatmap Using Multi-Lead ECGs.Bioengineering (Basel, Switzerland) · 2024Article
- An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review.Journal of clinical medicine · 2024Review
- Introduction and Comparison of Novel Decentral Learning Schemes with Multiple Data Pools for Privacy-Preserving ECG Classification.Journal of healthcare informatics research · 2023Article
- A framework for comparative study of databases and computational methods for arrhythmia detection from single-lead ECG.Scientific reports · 2023Article
- Practical intelligent diagnostic algorithm for wearable 12-lead ECG via self-supervised learning on large-scale dataset.Nature communications · 2023Article
- An Arrhythmia Classification Model Based on Vision Transformer with Deformable Attention.Micromachines · 2023Article
- Current and Future Use of Artificial Intelligence in Electrocardiography.Journal of cardiovascular development and disease · 2023Review
- Atrial fibrillation classification based on the 2D representation of minimal subset ECG and a non-deep neural network.Frontiers in physiology · 2023Article
- Two-dimensional ECG-based cardiac arrhythmia classification using DSE-ResNet.Scientific reports · 2022Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electrocardiograms (ECGs) are widely used to clinically detect cardiac arrhythmias (CAs). They are also being used to develop computer-assisted methods for heart disease diagnosis. We have developed a convolution neural network model to detect and classify CAs, using a large 12-lead ECG dataset (6,877 recordings) provided by the China Physiological Signal Challenge (CPSC) 2018. Our model, which was ranked first in the challenge competition, achieved a median overall F1-score of 0.84 for the nine-type CA classification of CPSC2018's hidden test set of 2,954 ECG recordings. Further analysis showed that concurrent CAs were adequately predictive for 476 patients with multiple types of CA diagnoses in the dataset. Using only single-lead data yielded a performance that was only slightly worse than using the full 12-lead data, with leads aVR and V1 being the most prominent. We extensively consider these results in the context of their agreement with and relevance to clinical observations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.