ArticlePeerJ. Computer science2021
From ECG signals to images: a transformation based approach for deep learning.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 133 citations in OpenAlex.
- Visualizing Relaxation in Wearables: Multi-Domain Feature Fusion of HRV Using Fuzzy Recurrence Plots.Sensors (Basel, Switzerland) · 2025Article
- Evaluation and analysis of image compression effect on neural network-based heart rate classification.Scientific reports · 2025Article
- A comprehensive dataset of soccer event images for advancing automatic recognition systems.Data in brief · 2025Article
- Transfer Learning Strategies for Cardiovascular Disease Detection in ECG Imagery.Biomedical engineering and computational biology · 2025Article
- BlendNet: a blending-based convolutional neural network for effective deep learning of electrocardiogram signals.Frontiers in artificial intelligence · 2025Article
- Arrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms.Sensors (Basel, Switzerland) · 2024Article
- Comparative Analysis of Machine Learning and Deep Learning Algorithms for Assessing Agricultural Product Quality Using NIRS.Sensors (Basel, Switzerland) · 2024Article
- Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI.Sensors (Basel, Switzerland) · 2023Article
- Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT.Sensors (Basel, Switzerland) · 2023Article
- Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction.Sensors (Basel, Switzerland) · 2023Article
- Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review.Heliyon · 2023Review
- An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal.Diagnostics (Basel, Switzerland) · 2022Article
- A multi-label classification system for anomaly classification in electrocardiogram.Health information science and systems · 2022Article
- A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images.Biomedicines · 2022Article
- Gastrointestinal Tract Polyp Anomaly Segmentation on Colonoscopy Images Using Graft-U-Net.Journal of personalized medicine · 2022Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- ECG Classification Using Orthogonal Matching Pursuit and Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Vec2image: an explainable artificial intelligence model for the feature representation and classification of high-dimensional biological data by vector-to-image conversion.Briefings in bioinformatics · 2022Article
- ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration.Computers in biology and medicine · 2022Article
- ECG Data Analysis with Denoising Approach and Customized CNNs.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate electrical impulses in the ventricles of the heart. Different types of arrhythmias are associated with different patterns, which can be identified. An electrocardiogram (ECG) is the major analytical tool used to interpret and record ECG signals. ECG signals are nonlinear and difficult to interpret and analyze. We propose a new deep learning approach for the detection of VA. Initially, the ECG signals are transformed into images that have not been done before. Later, these images are normalized and utilized to train the AlexNet, VGG-16 and Inception-v3 deep learning models. Transfer learning is performed to train a model and extract the deep features from different output layers. After that, the features are fused by a concatenation approach, and the best features are selected using a heuristic entropy calculation approach. Finally, supervised learning classifiers are utilized for final feature classification. The results are evaluated on the MIT-BIH dataset and achieved an accuracy of 97.6% (using Cubic Support Vector Machine as a final stage classifier).
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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.