ReviewFrontiers in physiology2022
Review of Processing Pathological Vectorcardiographic Records for the Detection of Heart Disease.
Review in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 34 citations in OpenAlex.
- Development and Internal Validation of a Vectorcardiography-Augmented Model for 12-Month Major Adverse Cardiovascular Events in Chronic Heart Failure.Journal of medical systems · 2026Article
- Enhancing tertiary cardiology triage with vectorcardiographic features: a machine learning approach using real-world data.Clinics (Sao Paulo, Brazil) · 2026Article
- Multimodal Patient-Specific Identification of Atrial Flutter Circuits From ECG Time Series Using Explainable Machine Learning.IEEE journal of translational engineering in health and medicine · 2026Article
- Real-time vectorcardiography simulator system.PloS one · 2026Article
- Diabetes-Related Alterations in Vectorcardiographic Repolarization Geometry: Insights from Physiological and Machine-Learning Analysis.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Review of Non-Invasive Fetal Electrocardiography Monitoring Techniques.Sensors (Basel, Switzerland) · 2025Review
- Enhancing myocardial infarction detection with vectorcardiography: fusion-based comparative analysis of machine learning methods.Frontiers in physiology · 2025Article
- The Importance of Electrovectorcardiographic Assessment of Intraventricular Blocks and their Relationship with the Cardiac Cycle.Arquivos brasileiros de cardiologia · 2025Article
- The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review.JMIR cardio · 2024Article
- Impact of Intraventricular Blocks on Cardiac Cycle Dynamics: An Echocardiographic and Vectorcardiographic Analysis.Arquivos brasileiros de cardiologia · 2024Article
- Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Vectorcardiography (VCG) is another useful method that provides us with useful spatial information about the electrical activity of the heart. The use of vectorcardiography in clinical practice is not common nowadays, mainly due to the well-established 12-lead ECG system. However, VCG leads can be derived from standard 12-lead ECG systems using mathematical transformations. These derived or directly measured VCG records have proven to be a useful tool for diagnosing various heart diseases such as myocardial infarction, ventricular hypertrophy, myocardial scars, long QT syndrome, etc., where standard ECG does not achieve reliable accuracy within automated detection. With the development of computer technology in recent years, vectorcardiography is beginning to come to the forefront again. In this review we highlight the analysis of VCG records within the extraction of functional parameters for the detection of heart disease. We focus on methods of processing VCG functionalities and their use in given pathologies. Improving or combining current or developing new advanced signal processing methods can contribute to better and earlier detection of heart disease. We also focus on the most commonly used methods to derive a VCG from 12-lead ECG.
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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.