Background: In equine medicine, 12-lead electrocardiograms (ECGs) rarely are used, which may in part be a result of shortcomings in the existing guidelines for obtaining 12-lead ECGs in horses. The guidelines recommend placing the limb leads on the extremities, which is inappropriate because the ventricular mean electrical axis is then perpendicular to the limb leads, leading to large variations in ECG configuration even among healthy horses. From an electrophysiological point of view, the leads instead should be parallel to the electrical axis to minimize variability.
Objective: Develop an improved method for obtaining 12-lead ECGs in horses based on electrophysiology and cardiac electrical vectors relevant to horses.
Animals: Thirty-five healthy Standardbred horses.
Methods: Two ECGs obtained at rest; 1 ECG with the electrodes placed according to the method developed in the present study, the Copenhagen method, and 1 ECG following existing guidelines.
Results: In the Copenhagen method, we repositioned the limb electrodes to the thorax to better capture the electrical activity of the heart. Variation in the mean electrical axis decreased dramatically with the Copenhagen method (SD decreased from 24.6° to 1.6°, P < .001). Consequently, this new method provided stable ECGs with repeatable configurations.
Conclusions And Clinical Importance: With this novel method, the ECG is recorded with respect to the electric axis to fully realize the potential of 12-lead ECG in horses. The Copenhagen method delivered more consistent and reliable ECG recordings compared to existing guidelines. The Copenhagen method potentially allows for expanded use of 12-lead ECGs in equine medicine.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7848388 | PMC |
| http://dx.doi.org/10.1111/jvim.15980 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Cardioish: Lead-Based Feature Extraction for ECG Signals.
Diagnostics (Basel)
November 2024
Department of Digital Forensics Engineering, Technology Faculty, Firat University, 23200 Elazig, Turkey.
Electrocardiography (ECG) signals are commonly used to detect cardiac disorders, with 12-lead ECGs being the standard method for acquiring these signals. The primary objective of this research is to propose a new feature engineering model that achieves both high classification accuracy and explainable results using ECG signals. To this end, a symbolic language, named Cardioish, has been introduced.
View Article and Find Full Text PDFComputerized QT and QTc Measurements From Bedside ICU Monitors Are Similar to Those Derived From a Standard 12-Lead ECG.
Ann Noninvasive Electrocardiol
January 2025
ECG Monitoring Research Lab, Department of Physiological Nursing, School of Nursing, University of California San Francisco, San Francisco, California, USA.
Article Synopsis
- QT/QTc prolongation can lead to torsade de pointes, and while 12-lead ECGs are commonly used for assessment, they disrupt patient care; bedside monitors with QT/QTc software have been implemented in the ICU.
- A study assessed the accuracy of QT/QTc measurements from these bedside monitors using four leads compared to standard 12-lead ECGs, involving measurements taken within 30 minutes of each other.
- The results indicated good agreement between the two methods, suggesting that bedside monitor QT/QTc readings could effectively identify at-risk patients without needing as many 12-lead ECGs, though further research is needed with larger and non-ICU patient samples.
The feasibility of a serial 12‑lead ECG wireless patch in the hospital setting.
J Electrocardiol
January 2025
University of Rochester School of Nursing, USA; University of Rochester Medical Center, USA.
Background: Chest pain is the second most common reason to present to the emergency department in the United States, and the ECG is a first-line diagnostic tool for myocardial ischemia assessment. For patients with ongoing symptoms or unclear initial ECGs, guidelines recommend performing multiple standard ECGs at 15-30-min intervals during the first 1-2 h, which improves acute coronary syndrome (ACS) detection by 15 % and accelerates triage of high-risk ACS patients. However, obtaining serial ECG is not consistently practiced due to overcrowding and the limited technical abilities of current 12‑lead ECG machines.
View Article and Find Full Text PDFDevelopment and Multinational Validation of an Ensemble Deep Learning Algorithm for Detecting and Predicting Structural Heart Disease Using Noisy Single-lead Electrocardiograms.
medRxiv
October 2024
Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
Background And Aims: AI-enhanced 12-lead ECG can detect a range of structural heart diseases (SHDs) but has a limited role in community-based screening. We developed and externally validated a noise-resilient single-lead AI-ECG algorithm that can detect SHD and predict the risk of their development using wearable/portable devices.
Methods: Using 266,740 ECGs from 99,205 patients with paired echocardiographic data at Yale New Haven Hospital, we developed ADAPT-HEART, a noise-resilient, deep-learning algorithm, to detect SHD using lead I ECG.
Classification of underlying paroxysmal supraventricular tachycardia types using deep learning of sinus rhythm electrocardiograms.
Digit Health
September 2024
Department of Intelligence and Information, Seoul National University, Seoul, Republic of Korea.
Article Synopsis
- This study investigates the difficulty of obtaining ECGs in patients with paroxysmal supraventricular tachycardia and explores using deep learning to classify types of tachycardia based on sinus rhythm ECGs.
- Utilizing a modified deep learning model on data from 833 patients with AVNRT and 346 with concealed AVRT, the study found that pre-training the model improved classification performance significantly.
- The results indicate that while deep learning can help differentiate between AVNRT and concealed AVRT using sinus rhythm ECGs, the performance is still modest, suggesting room for further improvement in non-invasive diagnoses.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!