Signals such as Complex Fractionated Atrial Electrograms (CFAE) are tracked during ablation procedures to locate the arrhythmical substrate regions. Most of CFAE classification tools use fractionation indexes. However, recordings from intracardiac catheter depend on electrode contact quality. This paper investigates the impact of electrode contact area on fractionation indexes. It is assessed through three kinds of arrhythmical activations resulting from a numerical simulation of a small piece of the cardiac tissue. Bipolar electrograms are extracted corresponding to 25 different contact areas and fractionation indexes (Shannon entropy, non linear energy operator and maximum peak ratio) are computed. Results yield that the Shannon entropy offers a good potential discrimination between arrhythmic scenarios and is less sensitive to the electrode contact variation.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/EMBC.2013.6610474 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Predicting Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction by Using Machine Learning.
JACC Asia
December 2024
Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital at Linkou, and Chang Gung University College of Medicine, Taoyuan, Taiwan.
Background: Few studies have incorporated echocardiography and laboratory data to predict clinical outcomes in heart failure with preserved ejection fraction (HFpEF).
Objectives: This study aimed to use machine learning to find predictors of heart failure (HF) hospitalization and cardiovascular (CV) death in HFpEF.
Methods: From the Chang Gung Research Database in Taiwan, 6,092 HFpEF patients (2,898 derivation, 3,194 validation) identified between 2008 and 2017 were followed until 2019.
A very rare phenotype of immunoglobulin G4-related disease that was manifested as constrictive pericarditis: a case report.
Eur Heart J Case Rep
January 2025
Department of Cardiovascular Medicine, Kyoto Chubu Medical Center, 25, Yagi-Ueno, Yagi-cho, Nantan City, Kyoto 629-0197, Japan.
Background: Constrictive pericarditis (CP) can arise from various causes, including post-operative degeneration, tuberculosis, and sequelae of pericarditis. Immunoglobulin (Ig) G4-related disease is a rare but recognized cause of CP. However, the specific mechanisms underlying these aetiologies and pathologies remain unclear.
View Article and Find Full Text PDFAssociation between hypoxemia and quality of life in patients with heart failure with preserved ejection fraction and sleep-disordered breathing.
Sleep Biol Rhythms
January 2025
Sleep Medicine Center, Saiseikai Futsukaichi Hospital, 3-13-1 Yumachi, Chikushino, Fukuoka, 818-8516 Japan.
Unlabelled: Sleep-disordered breathing is common among patients with heart failure with preserved ejection fraction (HFpEF), and might impact their quality of life due to nighttime hypoxemia and awakenings. However, the factors contributing to deterioration in quality of life remain unclear. This study investigated the factors associated with quality of life deterioration in patients with HFpEF and sleep-disordered breathing.
View Article and Find Full Text PDFPrognostic Value of LV Global Longitudinal Strain by 2D and 3D Speckle-Tracking Echocardiography in Patients With HFpEF.
Circ Cardiovasc Imaging
January 2025
Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (Y. Lin, M.X., L.Z., Y.Z., P.Z., X.C., M.J., L.G., Q.H., Z.W., Y.Y., Y. Li).
Background: In patients with heart failure with preserved ejection fraction (HFpEF), the impact of type 2 diabetes (T2D) on left ventricular global longitudinal strain (LV GLS) and its prognostic implications remains unclear. We aimed to evaluate LV function using two-dimensional (2D) and three-dimensional (3D) speckle-tracking echocardiography in patients with HFpEF with and without T2D, and to investigate its prognostic significance.
Methods: A total of 335 patients with HFpEF were prospectively enrolled for echocardiographic evaluation.
Evaluation of a machine learning-based metabolic marker for coronary artery disease in the UK Biobank.
Atherosclerosis
December 2024
The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Center for Genomic Data Analytics, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address:
Background And Aims: An in silico quantitative score of coronary artery disease (ISCAD), built using machine learning and clinical data from electronic health records, has been shown to result in gradations of risk of subclinical atherosclerosis, coronary artery disease (CAD) sequelae, and mortality. Large-scale metabolite biomarker profiling provides increased portability and objectivity in machine learning for disease prediction and gradation. However, these models have not been fully leveraged.
View Article and Find Full Text PDFWant 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!