Arrhythmias

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 of positive samples and other factors, there is currently no algorithm that is comparable to human experts in ECG morphological recognition. Our objective is to develop an ECG specialist-level deep learning method that can accurately identify ten ECG...

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 potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and technologies have enabled fECG monitoring from the early stages of pregnancy through fECG extraction from the combined fet...

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...

Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI.

BACKGROUND: Currently, patient selection in TAVI is based upon a multidisciplinary heart team assessment of patient comorbidities and surgical risk st...

Assessing the perforation site of cardiac tamponade during radiofrequency catheter ablation using gas analysis of pericardial effusion.

BACKGROUND: The incidence of pericardial effusion (PE) during radiofrequency catheter ablation (CA) for atrial fibrillation is approximately 1%. PE is...

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...

How Will Machine Learning Inform the Clinical Care of Atrial Fibrillation?

Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast...

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...

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...

Detecting cardiac pathologies via machine learning on heart-rate variability time series and related markers.

In this paper we develop statistical algorithms to infer possible cardiac pathologies, based on data collected from 24 h Holter recording over a sampl...

Performance of a convolutional neural network derived from an ECG database in recognizing myocardial infarction.

Artificial intelligence (AI) is developing rapidly in the medical technology field, particularly in image analysis. ECG-diagnosis is an image analysis...

Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms.

Deep neural networks (DNN) are state-of-the-art machine learning algorithms that can be learned to self-extract significant features of the electrocar...

A closer look to the new frontier of artificial intelligence in the percutaneous treatment of primary lesions of the liver.

The purpose of thermal ablation is induction of tumor death by means of localized hyperthermia resulting in irreversible cellular damage. Ablative the...

SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal.

Atrial fibrillation is the most common arrhythmia and is associated with high morbidity and mortality from stroke, heart failure, myocardial infarctio...

Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks.

BACKGROUND The correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardiac abnormalities, and conventional computerized in...