ReviewHeliyon2023
Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review.
Review in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Multimodal Integration of Ambulatory ECG and Clinical Features for Sudden Cardiac Death and Pump Failure Death PredictionmedRxiv : the preprint server for health sciences · 2026Article
- Precise peak width estimation for solving key challenges in biosignal and spectral analysis.Scientific reports · 2026Article
- Quantum Angle-distance kernel for ECG classification and anomaly detection: a quantum-inspired framework for biomedical signal analysis.BioData mining · 2026Article
- Ensemble fuzzy multilayer neural perceptron with optimized feature selection for cardiac disease prediction using MRI and ECG data.Frontiers in physiology · 2026Article
- Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention.Biomedicines · 2025Review
- Electrocardiogram Abnormality Detection Using Machine Learning on Summary Data and Biometric Features.Diagnostics (Basel, Switzerland) · 2025Article
- Monitoring heart rhythms in adult males with X-linked ichthyosis using wearable technology: a feasibility study.Archives of dermatological research · 2025Article
- Comprehensive Analysis of Cardiovascular Diseases: Symptoms, Diagnosis, and AI Innovations.Bioengineering (Basel, Switzerland) · 2024Review
- Early heart disease prediction using feature engineering and machine learning algorithms.Heliyon · 2024Article
- Predicting intensive care unit-acquired weakness: A multilayer perceptron neural network approach.World journal of clinical cases · 2024Article
- Robust QRS detection based on simulated degenerate optical parametric oscillator-assisted neural network.Heliyon · 2024Article
- Enhancing ECG classification with continuous wavelet transform and multi-branch transformer.Heliyon · 2024Article
- ECG Forecasting System Based on Long Short-Term Memory.Bioengineering (Basel, Switzerland) · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.
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What OpenQuestion holds
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.