ArticleDiagnostics (Basel, Switzerland)2023
Deep-Learning-Based Arrhythmia Detection Using ECG Signals: A Comparative Study and Performance Evaluation.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Artificial intelligence across the cardiovascular diagnostic pathway: a case-based narrative review.Cardiovascular diagnosis and therapy · 2026Review
- A lightweight residual dilated temporal transformer block for ECG classification on edge devices.Scientific reports · 2026Article
- A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet.Scientific reports · 2026Article
- A neuromorphic approach to early arrhythmia detection.Scientific reports · 2025Article
- Advances in cardiovascular signal analysis with future directions: a review of machine learning and deep learning models for cardiovascular disease classification based on ECG, PCG, and PPG signals.Biomedical engineering letters · 2025Review
- A Machine Learning-Based Ransomware Detection Method for Attackers' Neutralization Techniques Using Format-Preserving Encryption.Sensors (Basel, Switzerland) · 2025Article
- Long-Term Arrhythmia Detection Using Portable ECG Patches in Stroke Patients: A Case Series.European journal of case reports in internal medicine · 2025Article
- Arrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms.Sensors (Basel, Switzerland) · 2024Article
- A hybrid cardiovascular arrhythmia disease detection using ConvNeXt-X models on electrocardiogram signals.Scientific reports · 2024Article
- A pulmonary hypertension targeted algorithm to improve referral to right heart catheterization: A machine learning approach.Computational and structural biotechnology journal · 2024Article
- Review
- Paper-Recorded ECG Digitization Method with Automatic Reference Voltage Selection for Telemonitoring and Diagnosis.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart diseases is the world's principal cause of death, and arrhythmia poses a serious risk to the health of the patient. Electrocardiogram (ECG) signals can be used to detect arrhythmia early and accurately, which is essential for immediate treatment and intervention. Deep learning approaches have played an important role in automatically identifying complicated patterns from ECG data, which can be further used to identify arrhythmia. In this paper, deep-learning-based methods for arrhythmia identification using ECG signals are thoroughly studied and their performances evaluated on the basis of accuracy, specificity, precision, and F1 score. We propose the development of a small CNN, and its performance is compared against pretrained models like GoogLeNet. The comparative study demonstrates the promising potential of deep-learning-based arrhythmia identification using ECG signals.
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Registered trials
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