Arrhythmias

In situ tissue classification during laser ablation using acoustic signals.

We suggest a novel method to classify the type of tissue that is being ablated, using the recorded a...

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

Automatic classification of electrocardiogram (ECG) signals is important for diagnosing heart arrhyt...

Neural network model of an amphibian ventilatory central pattern generator.

The neuronal multiunit model presented here is a formal model of the central pattern generator (CPG)...

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia.

Early defibrillation by an automated external defibrillator (AED) is key for the survival of out-of-...

Ventricular geometry-regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography.

Up to one-third of patients selected by current guidelines do not respond to cardiac resynchronizati...

Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China.

INTRODUCTION: Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable car...

A new approach for arrhythmia classification using deep coded features and LSTM networks.

BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signal...

A RR interval based automated apnea detection approach using residual network.

BACKGROUND AND OBJECTIVE: Apnea is one of the most common conditions that causes sleep-disorder brea...

An Efficient Cardiac Arrhythmia Onset Detection Technique Using a Novel Feature Rank Score Algorithm.

The interpretation of various cardiovascular blood flow abnormalities can be identified using Electr...

Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network.

Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are used to help ...

Assessment of Electrocardiogram Rhythms by GoogLeNet Deep Neural Network Architecture.

The aim of this study is to design GoogLeNet deep neural network architecture by expanding the kerne...

Long-term results of monopolar versus bipolar radiofrequency ablation procedure for atrial fibrillation.

BACKGROUND: In this study, we aimed to evaluate the long-term outcomes of monopolar or bipolar radio...

A novel ECG signal compression method using spindle convolutional auto-encoder.

BACKGROUND AND OBJECTIVES: With rapid development of telehealth system and cloud platform, tradition...

ECG Multilead Interval Estimation Using Support Vector Machines.

This work reports a multilead interval measurement algorithm for a high-resolution digital electroc...

A Novel CNN-Based Framework for Classification of Signal Quality and Sleep Position from a Capacitive ECG Measurement.

The further exploration of the capacitive ECG (cECG) is hindered by frequent fluctuations in signal ...

A Supervised Approach to Robust Photoplethysmography Quality Assessment.

Early detection of Atrial Fibrillation (AFib) is crucial to prevent stroke recurrence. New tools for...

Cardiac Rhythm Device Identification Using Neural Networks.

OBJECTIVES: This paper reports the development, validation, and public availability of a new neural ...

Comparisons of efficacy, safety, and recurrence risk factors of paroxysmal and persistent atrial fibrillation catheter ablation using robotic magnetic navigation system.

BACKGROUND: No data exist on comparisons of efficacy, safety, and recurrence risk factors of paroxys...

Artificial intelligence-augmented ECG assessment: The promise and the challenge.

Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy (HCM-VAr-Risk Model).

Clinical risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HC) empl...

Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach.

BACKGROUND: Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by co...