Myocardial Infarction

Synthesis of Electrocardiogram V-Lead Signals From Limb-Lead Measurement Using R-Peak Aligned Generative Adversarial Network.

Recently, portable electrocardiogram (ECG) hardware devices have been developed using limb-lead measurements. However, portable ECGs provide insufficient ECG information because of limitations in the number of leads and measurement positions. Therefore, in this study, V-lead ECG signals were synthesized from limb leads using an R-peak aligned generative adversarial network (GAN). The data used the...

SPICED-ACS: Study of the potential impact of a computer-generated ECG diagnostic algorithmic certainty index in STEMI diagnosis: Towards transparent AI.

BACKGROUND: Computerised electrocardiogram (ECG) interpretation diagnostic algorithms have been developed to guide clinical decisions like with ST segment elevation myocardial infarction (STEMI) where time in decision making is critical. These computer-generated diagnoses have been proven to strongly influence the final ECG diagnosis by the clinician; often called automation bias. However, the com...

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

Early detection of coronary heart disease (CHD) has the potential to prevent the millions of deaths that this disease causes worldwide every year. How...

Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals.

In this study, a deep-transfer learning approach is proposed for the automated diagnosis of diabetes mellitus (DM), using heart rate (HR) signals obta...

Cardiac arrhythmia detection using deep learning: A review.

Due to its simplicity and low cost, analyzing an electrocardiogram (ECG) is the most common technique for detecting cardiac arrhythmia. The massive am...

Machine learning in the electrocardiogram.

The electrocardiogram is the most widely used diagnostic tool that records the electrical activity of the heart and, therefore, its use for identifyin...

Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network.

Atrial fibrillation (AF) is the most common heart arrhythmia, and 12-lead electrocardiogram (ECG) is regarded as the gold standard for AF diagnosis. H...

An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction.

BACKGROUND: Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing sc...

Multicenter experience with photoselective vaporization of the prostate on men taking novel oral anticoagulants.

OBJECTIVE: Photoselective vaporization of the prostate (PVP) is a widely performed surgical procedure for benign prostatic obstruction. This approach ...

Non-contact heart and respiratory rate monitoring of preterm infants based on a computer vision system: a method comparison study.

BACKGROUND: Non-contact heart rate (HR) and respiratory rate (RR) monitoring is necessary for preterm infants due to the potential for the adhesive el...

Energy-Efficient Intelligent ECG Monitoring for Wearable Devices.

Wearable intelligent ECG monitoring devices can perform automatic ECG diagnosis in real time and send out alert signal together with abnormal ECG sign...

A Novel Approach for Multi-Lead ECG Classification Using DL-CCANet and TL-CCANet.

Cardiovascular disease (CVD) has become one of the most serious diseases that threaten human health. Over the past decades, over 150 million humans ha...

Is Cardiac Troponin I Valuable to Detect Low-Level Myocardial Damage in Congestive Heart Failure?

OBJECTIVES: Congestive heart failure (CHF) is a heart disease with a growing incidence and prevalence. Creatine kinase-myocardial base (CK-MB) is gene...

Comparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention.

IMPORTANCE: Better prediction of major bleeding after percutaneous coronary intervention (PCI) may improve clinical decisions aimed to reduce bleeding...

Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention.

OBJECTIVES: This study sought to determine whether machine learning can be used to better identify patients at risk for death or congestive heart fail...

Novel Metric Using Laplacian Eigenmaps to Evaluate Ischemic Stress on the Torso Surface.

The underlying pathophysiology of myocardial ischemia is incompletely understood, resulting in persistent difficulty of diagnosis. This limited unders...

Adjusting the dose in paediatric care: dispersing four different aspirin tablets and taking a proportion.

OBJECTIVES: When caring for children in a hospital setting, tablets are often manipulated at the ward to obtain the right dose. One example is manipul...

Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network.

The classification of electrocardiograms (ECG) plays an important role in the clinical diagnosis of heart disease. This paper proposes an effective sy...

Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings.

OBJECTIVE: We aim to combine deep neural networks and engineered features (hand-crafted features based on medical domain knowledge) for cardiac arrhyt...

I-Vector-Based Patient Adaptation of Deep Neural Networks for Automatic Heartbeat Classification.

Automatic classification of electrocardiogram (ECG) signals is important for diagnosing heart arrhythmias. A big challenge in automatic ECG classifica...