ArticleBiomedicines2022
Artificial Intelligence for Cardiac Diseases Diagnosis and Prediction Using ECG Images on Embedded Systems.
Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Applications in Medical Devices for Personalized Health Care Solutions: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Cardiovascular Disease Risk Assessment from Retinal Fundus Images Using a CSP-ConvNext Model.Diagnostics (Basel, Switzerland) · 2026Article
- A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet.Scientific reports · 2026Article
- Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach.Scientific reports · 2025Article
- Enhancing Heart Disease Diagnosis Using ECG Signal Reconstruction and Deep Transfer Learning Classification with Optional SVM Integration.Diagnostics (Basel, Switzerland) · 2025Article
- Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets.Sensors (Basel, Switzerland) · 2024Article
- The association between continuous metabolic syndrome score and its components with electrocardiographic abnormalities in community-dwelling older adults: the Bushehr elderly health (BEH) program.BMC cardiovascular disorders · 2024Article
- Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases.Diagnostics (Basel, Switzerland) · 2024Article
- Cardiovascular Diseases Diagnosis Using an ECG Multi-Band Non-Linear Machine Learning Framework Analysis.Bioengineering (Basel, Switzerland) · 2024Article
- CARDPSoML: Comparative approach to analyze and predict cardiovascular disease based on medical report data and feature fusion approach.Health science reports · 2024Article
- Mud Ring Optimization Algorithm with Deep Learning Model for Disease Diagnosis on ECG Monitoring System.Sensors (Basel, Switzerland) · 2023Article
- Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction.Sensors (Basel, Switzerland) · 2023Article
- A Robust Framework for Data Generative and Heart Disease Prediction Based on Efficient Deep Learning Models.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The electrocardiogram (ECG) provides essential information about various human cardiac conditions. Several studies have investigated this topic in order to detect cardiac abnormalities for prevention purposes. Nowadays, there is an expansion of new smart signal processing methods, such as machine learning and its sub-branches, such as deep learning. These popular techniques help analyze and classify the ECG signal in an efficient way. Our study aims to develop algorithmic models to analyze ECG tracings to predict cardiovascular diseases. The direct impact of this work is to save lives and improve medical care with less expense. As health care and health insurance costs increase in the world, the direct impact of this work is saving lives and improving medical care. We conducted numerous experiments to optimize deep-learning parameters. We found the same validation accuracy value of about 0.95 for both MobileNetV2 and VGG16 algorithms. After implementation on Raspberry Pi, our results showed a small decrease in accuracy (0.94 and 0.90 for MobileNetV2 and VGG16 algorithms, respectively). Therefore, the main purpose of the present research work is to improve, in an easy and cheaper way, real-time monitoring using smart mobile tools (mobile phones, smart watches, connected T-shirts, etc.).
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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.