Arrhythmias

Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals.

Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is critica...

Generalized Generative Deep Learning Models for Biosignal Synthesis and Modality Transfer.

Generative Adversarial Networks (GANs) are a revolutionary innovation in machine learning that enabl...

Deep Learning of ECG for the Prediction of Postoperative Atrial Fibrillation.

A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge.

Automated electrocardiogram (ECG) classification using machine learning (ML) is extensively utilized...

Electrocardiogram Detection of Pulmonary Hypertension Using Deep Learning.

BACKGROUND: Pulmonary hypertension (PH) is life-threatening, and often diagnosed late in its course....

The predictive value of deep learning-based cardiac ultrasound flow imaging for hypertrophic cardiomyopathy complicating arrhythmias.

OBJECTIVE: To investigate the predictive value of deep learning-based cardiac ultrasound flow imagin...

Application of machine learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia.

BACKGROUND AND OBJECTIVE: In silico prediction of drug-induced ventricular arrhythmia often requires...

Unsupervised ECG Analysis: A Review.

Electrocardiography is the gold standard technique for detecting abnormal heart conditions. Automati...

Smartwatch Sensors with Deep Learning to Predict the Purchase Intentions of Online Shoppers.

In the past decade, the scale of e-commerce has continued to grow. With the outbreak of the COVID-19...

Diagnosis of arrhythmias with few abnormal ECG samples using metric-based meta learning.

A major challenge in artificial intelligence based ECG diagnosis lies that it is difficult to obtain...

A deep learning platform to assess drug proarrhythmia risk.

Drug safety initiatives have endorsed human iPSC-derived cardiomyocytes (hiPSC-CMs) as an in vitro m...

Role of deep learning methods in screening for subcutaneous implantable cardioverter defibrillator in heart failure.

INTRODUCTION: S-ICD eligibility is assessed at pre-implant screening where surface ECG traces are us...

A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network.

The non-invasive electrocardiogram (ECG) signals are useful in heart condition assessment and are fo...

Obstructive Sleep Apnea Detection Scheme Based on Manually Generated Features and Parallel Heterogeneous Deep Learning Model Under IoMT.

Obstructive sleep apnea (OSA) syndrome is a common sleep disorder and a key cause of cardiovascular ...

Diffusion-weighted MRI with deep learning for visualizing treatment results of MR-guided HIFU ablation of uterine fibroids.

OBJECTIVES: No method is available to determine the non-perfused volume (NPV) repeatedly during magn...

A fully-automated paper ECG digitisation algorithm using deep learning.

There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recen...

Fixed-Time Synchronization of Coupled Oscillator Networks with a Pacemaker.

This paper investigates the fixed-time synchronization problem of a Kuramoto-oscillator network in t...

A joint cross-dimensional contrastive learning framework for 12-lead ECGs and its heterogeneous deployment on SoC.

The utilization of unlabeled electrocardiogram (ECG) data is always a critical topic in artificial i...

Understanding Neural Networks and Individual Neuron Importance via Information-Ordered Cumulative Ablation.

In this work, we investigate the use of three information-theoretic quantities-entropy, mutual infor...

Percutaneous Ablation Robot-Assisted Partial Nephrectomy for Completely Endophytic Renal Masses: A Multicenter Trifecta Analysis with a Minimum 3-Year Follow-Up.

To compare outcomes of robot-assisted partial nephrectomy (RAPN) and percutaneous tumor ablation (P...

CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals.

Heart failure (HF) is a serious condition in which the heart fails to supply the body with enough ox...