Myocardial Infarction

Relationship between Coronary Iodine Concentration Determined Using Spectral CT and the Outcome of Percutaneous Coronary Intervention in Patients with Chronic Total Occlusion.

PURPOSE: To evaluate the feasibility of coronary iodine concentration (CIC) by using spectral CT in the assessment of the outcome of percutaneous coronary intervention (PCI) for chronic total occlusion (CTO).

Machine learning techniques for detecting electrode misplacement and interchanges when recording ECGs: A systematic review and meta-analysis.

INTRODUCTION: Electrode misplacement and interchange errors are known problems when recording the 12‑lead electrocardiogram (ECG). Automatic detection of these errors could play an important role for improving clinical decision making and outcomes in cardiac care. The objectives of this systematic review and meta-analysis is to 1) study the impact of electrode misplacement on ECG signals and ECG i...

CNN and LSTM-Based Emotion Charting Using Physiological Signals.

Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG)...

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram.

Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during ...

Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea.

BACKGROUND: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The...

A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling.

Three-dimensional speckle tracking echocardiography (3D STE) is an emerging noninvasive method for predicting left ventricular remodeling (LVR) after ...

ECG Biometrics Using Deep Learning and Relative Score Threshold Classification.

The field of biometrics is a pattern recognition problem, where the individual traits are coded, registered, and compared with other database records....

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea.

Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysfunction, and neuromuscular disorder, among other c...

Using the VQ-VAE to improve the recognition of abnormalities in short-duration 12-lead electrocardiogram records.

BACKGROUND AND OBJECTIVE: Morphological diagnosis is a basic clinical task of the short-duration 12-lead electrocardiogram (ECG). Due to the scarcity ...

An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram.

The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potent...

Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques.

Continuous blood pressure (BP) measurement is crucial for reliable and timely hypertension detection. State-of-the-art continuous BP measurement metho...

Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices.

Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (E...

Missing Value Estimation Methods Research for Arrhythmia Classification Using the Modified Kernel Difference-Weighted KNN Algorithms.

Electrocardiogram (ECG) signal is critical to the classification of cardiac arrhythmia using some machine learning methods. In practice, the ECG datas...

Artificial Intelligence-Enabled ECG: a Modern Lens on an Old Technology.

PURPOSE OF REVIEW: To (i) review the concept of artificial intelligence (AI); (ii) summarize recent developments in artificial intelligence-enabled el...

Machine Learning of 12-Lead QRS Waveforms to Identify Cardiac Resynchronization Therapy Patients With Differential Outcomes.

BACKGROUND: Cardiac resynchronization therapy (CRT) improves heart failure outcomes but has significant nonresponse rates, highlighting limitations in...

In-Hospital Prognostic Value of Electrocardiographic Parameters Other Than ST-Segment Changes in Acute Myocardial Infarction: Literature Review and Future Perspectives.

Electrocardiography (ECG) remains an irreplaceable tool in the management of the patients with myocardial infarction, with evaluation of the QRS and S...

Localization of origins of premature ventricular contraction in the whole ventricle based on machine learning and automatic beat recognition from 12-lead ECG.

OBJECTIVE: The localization of origins of premature ventricular contraction (PVC) is the key factor for the success of ablation of ventricular arrhyth...

Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review.

BACKGROUND: The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achie...

Defining heterogeneity of epicardial functional stenosis with low coronary flow reserve by unsupervised machine learning.

Low CFR is associated with poor prognosis, whereas it is a heterogeneous condition according to the actual coronary flow, such as high resting or low ...

Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study.

BACKGROUND: Market-applicable concurrent electrocardiogram (ECG) diagnosis for multiple heart abnormalities that covers a wide range of arrhythmias, w...