ArticleEntropy (Basel, Switzerland)2021
Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
- A deep learning method for preterm birth prediction using wavelet representations and auxiliary features from electrohysterogram.Medical & biological engineering & computing · 2026Article
- Compact neural network algorithm for electrocardiogram classification.Physical and engineering sciences in medicine · 2026Article
- WVM-UNet: A Wavelet-Vision Mamba Framework for Enhanced Medical Image Segmentation.Journal of imaging · 2026Article
- A Cascaded Quantized Spiking Neural Network for Real-Time ECG Arrhythmia Detection on Edge Hardware.Sensors (Basel, Switzerland) · 2026Article
- Towards Feasible Home ECG Monitoring: AI-Driven Detection of Clinically Critical Arrhythmias Using Single-Lead Signals.Bioengineering (Basel, Switzerland) · 2026Article
- Comparative evaluation of deep learning models for cardiovascular disease diagnosis and classification.Scientific reports · 2026Article
- Wavelet Transform and Hierarchical Hybrid Matching for Enhancing End-to-End Pediatric Wrist Fracture Detection.Journal of imaging informatics in medicine · 2026Article
- MnL-TWA: Manifold Learning Approach for T-Wave Alternans Detection in Ambulatory Environments.Biomedical engineering and computational biology · 2026Article
- Article
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- Lubrication state identification of vibration time-frequency characteristics based on CWT and CNN.Scientific reports · 2025Article
- Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques.Scientific reports · 2025Article
- Explainable AI for Intraoperative Motor-Evoked Potential Muscle Classification in Neurosurgery: Bicentric Retrospective Study.Journal of medical Internet research · 2025Article
- [Research on arrhythmia classification algorithm based on adaptive multi-feature fusion network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN.Journal of biomedical physics & engineering · 2025Article
- Multi-Scale Analysis of Knee Joint Acoustic Signals for Cartilage Degeneration Assessment.Sensors (Basel, Switzerland) · 2025Article
- Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.BMC medical informatics and decision making · 2025Article
- Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model.Diagnostics (Basel, Switzerland) · 2025Article
- Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach.PloS one · 2025Article
- BlendNet: a blending-based convolutional neural network for effective deep learning of electrocardiogram signals.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.
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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.