Arrhythmias

Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation.

OBJECTIVE: The prevalence of atrial fibrillation (AF) in the general population is 0.5%-1%. As AF is...

Temporal Performance of Laplacian Eigenmaps and 3D Conduction Velocity in Detecting Ischemic Stress.

BACKGROUND: Myocardial ischemia has a complex and time-varying electrocardiographic signature that i...

Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings.

The development of new technology such as wearables that record high-quality single channel ECG, pro...

Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings.

Atrial fibrillation (AF) is one of the most common sustained chronic cardiac arrhythmia in elderly p...

Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine.

Electrocardiogram (ECG) classification is an important process in identifying arrhythmia, and neural...

Ablation of NTPDase2+ cells inhibits the formation of filiform papillae in tongue tip.

BACKGROUND: Lingual epithelia in the tongue tip are among the most rapidly regenerating tissues, but...

Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor.

Routine stress monitoring in daily life can predict potentially serious health impacts. Effective st...

Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks.

Atrial fibrillation (AF) is a serious cardiovascular disease with the phenomenon of beating irregula...

A support vector machine approach for AF classification from a short single-lead ECG recording.

OBJECTIVE: In this paper, a support vector machine (SVM) approach using statistical features, P wave...

Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals.

Obstructive sleep apnea (OSA) is a sleep disorder caused due to interruption of breathing resulting ...

A novel application of deep learning for single-lead ECG classification.

Detecting and classifying cardiac arrhythmias is critical to the diagnosis of patients with cardiac ...

Robotic magnetic catheter navigation as a first step in the automation of the ablation procedures for ventricular arrhythmia.

Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease.

Extracellular matrix (ECM) proteins have been shown to play important roles regulating multiple biol...

Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network.

In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from...

A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification.

Long-short term memory networks (LSTMs), which have recently emerged in sequential data analysis, ar...

Model-Based Feature Augmentation for Cardiac Ablation Target Learning From Images.

GOAL: We present a model-based feature augmentation scheme to improve the performance of a learning ...

Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants.

Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when ...

Randomized controlled trial of Amigo® robotically controlled versus manually controlled ablation of the cavo-tricuspid isthmus using a contact force ablation catheter.

BACKGROUND: Radiofrequency catheter ablation (RFCA) of the cavo-tricuspid isthmus (CTI) is a common ...

Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

BACKGROUND: Stress recognition using electrocardiogram (ECG) signals requires the intractable long-t...

Automatic QRS complex detection using two-level convolutional neural network.

BACKGROUND: The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, th...

Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout.

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose ...