Cardiology Commons^™

Automated Classification Model With Otsu And Cnn Method For Premature Ventricular Contraction Detection, Liang-Hung Wang, Lin-Juan Ding, Chao-Xin Xie, Su-Ya Jiang, I-Chun Kuo, Xin-Kang Wang, Jie Gao, Pao-Cheng Huang, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requiring a professional medical diagnosis. This study proposes a novel method based on image processing and convolutional neural network (CNN) to extract electrocardiography (ECG) curves from scanned ECG images derived from clinical ECG reports, and segment and classify heartbeats in the absence of a digital ECG data. The ECG curve is extracted using a comprehensive algorithm …

Hiv Infection And The Risk Of World Health Organization-Defined Sudden Cardiac Death, Matthew S. Freiberg, Meredith S. Duncan, Charles Alcorn, Chung-Chou H. Chang, Suman Kundu, Asri Mumpuni, Emily K. Smith, Sarah Loch, Annie Bedigian, Eric Vittinghoff, Kaku So-Armah, Priscilla Y. Hsue, Amy C. Justice, Zian H. Tseng

Biostatistics Faculty Publications

Background

People living with HIV have higher sudden cardiac death (SCD) rates compared with the general population. Whether HIV infection is an independent SCD risk factor is unclear.

Methods and Results

This study evaluated participants from the Veterans Aging Cohort Study, an observational, longitudinal cohort of veterans with and without HIV infection matched 1:2 on age, sex, race/ethnicity, and clinical site. Baseline for this study was a participant's first clinical visit on or after April 1, 2003. Participants were followed through December 31, 2014. Using Cox proportional hazards regression, we assessed whether HIV infection, CD4 cell counts, and/or HIV viral …

Diagnostic Accuracy Of Machine Learning Models To Identify Congenital Heart Disease: A Meta-Analysis, Zahra Hoodbhoy, Uswa Jiwani, Saima Sattar, Rehana A. Salam, Babar Hasan, Jai K. Das

Department of Paediatrics and Child Health

Background: With the dearth of trained care providers to diagnose congenital heart disease (CHD) and a surge in machine learning (ML) models, this review aims to estimate the diagnostic accuracy of such models for detecting CHD.
Methods: A comprehensive literature search in the PubMed, CINAHL, Wiley Cochrane Library, and Web of Science databases was performed. Studies that reported the diagnostic ability of ML for the detection of CHD compared to the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 tool. The sensitivity and specificity results from the studies were used to …

Cardiovascular Complications Of Systemic Lupus Erythematosus: Impact Of Risk Factors And Therapeutic Efficacy--A Tertiary Centre Experience In An Appalachian State, Elise Danielle Mcveigh, Amna Batool, Arnold J. Stromberg, Ahmed K. Abdel-Latif, Nayef Mohammed Kazzaz

Statistics Faculty Publications

OBJECTIVES: Cardiovascular complications became a notable cause of morbidity and mortality in patients with lupus as therapeutic advancements became more efficient at managing other complications. The Appalachian community in Kentucky has a higher prevalence of traditional cardiovascular risk factors, predisposing them to cardiovascular events. Namely, the mean body mass index of the members of the Kentucky Appalachian community was reported at 33 kg/m² and 94.3% of male members of this community use tobacco. We sought to identify risk factors that predispose patients with lupus to cardiovascular morbidities and examine the effect of immunomodulatory drugs.

METHODS: We identified 20 UKHS …

Detection Of Myocardial Infarction Using Ecg And Multi-Scale Feature Concatenate, Jia-Zheng Jian, Tzong-Rong Ger, Han-Hua Lai, Chi-Ming Ku, Chiung-An Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However; issues; particularly overfitting and underfitting; were not being taken into account. In other words; it is unclear whether the network structure is too simple or complex. Toward this end; the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally; multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being …

A High-Precision Machine Learning Algorithm To Classify Left And Right Outflow Tract Ventricular Tachycardia, Jianwei Zhang, Guohua Fu, Islam Abudayyeh, Magdi Yacoub, Anthony Chang, William Feaster, Louis Ehwerhemuepha, Hesham El-Askary, Xianfeng Du, Bin He, Mingjun Feng, Yibo Yu, Binhao Wang, Jing Liu, Hai Yao, Hulmin Chu, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Introduction: Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model.

Methods: We randomly sampled training, validation, and testing …