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| http://dx.doi.org/10.1093/ehjcr/ytad094 | DOI Listing |
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The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review.
JMIR Cardio
December 2024
School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
Background: Cardiovascular disease remains the leading cause of mortality worldwide. Cardiac fibrosis impacts the underlying pathophysiology of many cardiovascular diseases by altering structural integrity and impairing electrical conduction. Identifying cardiac fibrosis is essential for the prognosis and management of cardiovascular disease; however, current diagnostic methods face challenges due to invasiveness, cost, and inaccessibility.
View Article and Find Full Text PDFAutomated differentiation of wide QRS complex tachycardia using QRS complex polarity.
Commun Med (Lond)
December 2024
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Background: Wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) remains challenging despite numerous 12-lead electrocardiogram (ECG) criteria and algorithms. Automated solutions leveraging computerized ECG interpretation (CEI) measurements and engineered features offer practical ways to improve diagnostic accuracy. We propose automated algorithms based on (i) WCT QRS polarity direction (WCT Polarity Code [WCT-PC]) and (ii) QRS polarity shifts between WCT and baseline ECGs (QRS Polarity Shift [QRS-PS]).
View Article and Find Full Text PDFThe role of telemetry monitoring: From diagnosing arrhythmia to predictive models of patient instability.
J Electrocardiol
December 2024
Emory University, Atlanta, GA, USA. Electronic address:
Over the past sixty years, telemetry monitoring has become integral to hospital care, offering critical insights into patient health by tracking key indicators like heart rate, respiratory rate, blood pressure, and oxygen saturation. Its primary application, continuous electrocardiographic (ECG) monitoring, is essential in diverse settings such as emergency departments, step-down units, general wards, and intensive care units for the early detection of cardiac rhythms signaling acute clinical deterioration. Recent advancements in data analytics and machine learning have expanded telemetry's role from observation to prognostication, enabling predictive models that forecast inhospital events indicative of patient instability.
View Article and Find Full Text PDFAn improved electrocardiogram arrhythmia classification performance with feature optimization.
BMC Med Inform Decis Mak
December 2024
Intelligent System Research Group, Faculty of Computer Science Universitas Sriwijaya, Palembang, 30139, Indonesia.
Background: Automatic classification of arrhythmias based on electrocardiography (ECG) data faces several significant challenges, particularly due to the substantial volume of clinical data involved in ECG signal analysis. The volume of clinical data has increased considerably, especially with the emergence of new clinical symptoms and signs in various arrhythmia conditions. These symptoms and signs, which serve as distinguishing features, can number in the tens of thousands.
View Article and Find Full Text PDFArtificial Intelligence-Driven Advances in Coronary Calcium Scoring: Expanding Preventive Cardiology.
Cureus
November 2024
General Surgery, Northeast Georgia Medical Center Gainesville, Gainesville, USA.
Coronary artery disease (CAD) remains a leading global cause of morbidity and mortality, underscoring the need for effective cardiovascular risk stratification and preventive strategies. Coronary artery calcium (CAC) scoring, traditionally performed using electrocardiogram (ECG)-gated cardiac computed tomography (CT) scans, has been widely validated as a robust tool for assessing cardiovascular risk. However, its application has been largely limited to high-risk populations due to the costs, technical requirements, and limited accessibility of cardiac CT scans.
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