ArticlePloS one2023
ECG classification using 1-D convolutional deep residual neural network.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 77 citations in OpenAlex.
- Intelligent Real-Time Healthcare and Biomedical Monitoring Systems: A Narrative Review of AI, IoT, and Emerging Technologies.Bioengineering (Basel, Switzerland) · 2026Review
- Compact neural network algorithm for electrocardiogram classification.Physical and engineering sciences in medicine · 2026Article
- CaReS-BiNet: A multi-scale deep learning framework for ECG arrhythmia classification.Scientific reports · 2026Article
- MSCA-TNet based deep learning method for ECG arrhythmia classification.Scientific reports · 2026Article
- Machine learning-enabled ECG arrhythmia classification: a systematic and educational study from signal processing to decision support.Scientific reports · 2026Article
- The Rlign algorithm for enhanced electrocardiogram analysis through heart rate-corrected ECG alignment for explainable classification and clustering.European heart journal. Digital health · 2026Article
- AI based ECG data recovery and cardiovascular diseases classification (CEDRC-network).Scientific reports · 2026Article
- A deep residual 1D-CNN with self-attention for fraud transaction detection in virtual economies.Scientific reports · 2026Article
- A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet.Scientific reports · 2026Article
- Enhanced prediction of left ventricular ejection fraction using electrocardiography with the addition of clinical metadata.The Korean journal of internal medicine · 2026Article
- HPRNet: a hierarchical pyramidal residual network for ECG arrhythmia classification.Frontiers in physiology · 2026Article
- Explainable hybrid deep learning framework with Grad-CAM for heartbeat-level arrhythmia classification.Frontiers in physiology · 2026Article
- Bridging clinical knowledge and AI: an interpretable transformer framework for ECG diagnosis.NPJ digital medicine · 2025Article
- An integrated algorithm for single lead electrocardiogram signal analysis using deep learning with 12-lead data.Scientific reports · 2025Article
- Fully Automated Diagnosis of Acute Myocardial Infarction Using Electrocardiograms and Multimodal Deep Learning.JACC. Advances · 2025Article
- Prediction of Cerebrospinal Fluid (CSF) Pressure with Generative Adversarial Network Synthetic Plasma-CSF Biomarker Pairing.Neuroinformatics · 2025Article
- Efficient pretraining of ECG scalogram images using masked autoencoders for cardiovascular disease diagnosis.Scientific reports · 2025Article
- Principal component conditional generative adversarial networks for imbalanced ECG classification enhancement.PloS one · 2025Article
- BlendNet: a blending-based convolutional neural network for effective deep learning of electrocardiogram signals.Frontiers in artificial intelligence · 2025Article
- Artificial intelligence for detection of retinal toxicity in chloroquine and hydroxychloroquine therapy using multifocal electroretinogram waveforms.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning (DL) based system that employs the convolutional neural networks (CNNs) for classification of ECG signals present in PhysioNet MIT-BIH Arrhythmia database. The proposed system implements 1-D convolutional deep residual neural network (ResNet) model that performs feature extraction by directly using the input heartbeats. We have used synthetic minority oversampling technique (SMOTE) that process class-imbalance problem in the training dataset and effectively classifies the five heartbeat types in the test dataset. The classifier's performance is evaluated with ten-fold cross validation (CV) using accuracy, precision, sensitivity, F1-score, and kappa. We have obtained an average accuracy of 98.63%, precision of 92.86%, sensitivity of 92.41%, and specificity of 99.06%. The average F1-score and Kappa obtained were 92.63% and 95.5% respectively. The study shows that proposed ResNet performs well with deep layers compared to other 1-D CNNs.
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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.