Literature DB >> 27576235

Simple T-Wave Metrics May Better Predict Early Ischemia as Compared to ST Segment.

Glenn Terje Lines, Bernardo Lino de Oliveira, Ola Skavhaug, Mary M Maleckar.   

Abstract

There is pressing clinical need to identify developing heart attack (infarction) in patients as early as possible. However, current state-of-the-art tools in clinical practice, underpinned by the evaluation of elevation of the ST segment of the 12-lead electrocardiogram (ECG), do not identify all patients suffering from lack of blood flow to the heart muscle (cardiac ischemia), worsening the risk for further adverse events and patient outcome overall. In this study, we aimed to explore and compare the portions of cardiac repolarization in the ECG that best capture the electrophysiological changes associated with ischemia. We developed three-dimensional electrophysiological models of the human ventricles and torso, incorporating biophysically-based membrane kinetics and realistic activation sequence, to compute simulated ECGs and their alteration with the application of simulated ischemia of differing severity in diverse regions of the heart. Results suggest that metrics based on the T-wave in addition to the ST segment may be more sensitive to detecting ischemia than those using the ST segment alone. Further research into how such simulation-aided risk assessment methods may aid workflows in extant clinical practice, with the ultimate goal of multimodality clinical support, is warranted.

Entities:  

Mesh:

Year:  2016        PMID: 27576235     DOI: 10.1109/TBME.2016.2600198

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  6 in total

1.  A telediagnosis assistance system for multiple-lead electrocardiography.

Authors:  Paulo César Lucena Bentes; Jurandir Nadal
Journal:  Phys Eng Sci Med       Date:  2021-04-02

2.  A Framework for Image-Based Modeling of Acute Myocardial Ischemia Using Intramurally Recorded Extracellular Potentials.

Authors:  Brett M Burton; Kedar K Aras; Wilson W Good; Jess D Tate; Brian Zenger; Rob S MacLeod
Journal:  Ann Biomed Eng       Date:  2018-05-21       Impact factor: 3.934

3.  Novel ECG features and machine learning to optimize culprit lesion detection in patients with suspected acute coronary syndrome.

Authors:  Zeineb Bouzid; Ziad Faramand; Richard E Gregg; Stephanie Helman; Christian Martin-Gill; Samir Saba; Clifton Callaway; Ervin Sejdić; Salah Al-Zaiti
Journal:  J Electrocardiol       Date:  2021-07-23       Impact factor: 1.438

4.  A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG.

Authors:  Carlos A Ledezma; Xin Zhou; Blanca Rodríguez; P J Tan; Vanessa Díaz-Zuccarini
Journal:  PLoS One       Date:  2019-08-12       Impact factor: 3.240

5.  In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.

Authors:  Zeineb Bouzid; Ziad Faramand; Richard E Gregg; Stephanie O Frisch; Christian Martin-Gill; Samir Saba; Clifton Callaway; Ervin Sejdić; Salah Al-Zaiti
Journal:  J Am Heart Assoc       Date:  2021-01-17       Impact factor: 5.501

6.  Evaluating Morphological Features of Electrocardiogram Signals for Diagnosing of Myocardial Infarction Using Classification-Based Feature Selection.

Authors:  Seyed Ataddin Mahmoudinejad; Naser Safdarian
Journal:  J Med Signals Sens       Date:  2021-05-24
  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.