Arrhythmias

Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals.

The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural network trained jointly with the linear mapping at the encoder. The divination of the oracle is then used to estimate the coefficients by pseudo-inversion...

Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography.

Background Severe, symptomatic aortic stenosis (AS) is associated with poor prognoses. However, early detection of AS is difficult because of the long asymptomatic period experienced by many patients, during which screening tools are ineffective. The aim of this study was to develop and validate a deep learning-based algorithm, combining a multilayer perceptron and convolutional neural network, fo...

Substrate Spatial Complexity Analysis for the Prediction of Ventricular Arrhythmias in Patients With Ischemic Cardiomyopathy.

BACKGROUND: Transition zones between healthy myocardium and scar form a spatially complex substrate that may give rise to reentrant ventricular arrhyt...

Non-Standardized Patch-Based ECG Lead Together With Deep Learning Based Algorithm for Automatic Screening of Atrial Fibrillation.

This study was to assess the feasibility of using non-standardized single-lead electrocardiogram (ECG) monitoring to automatically detect atrial fibri...

Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal.

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineeri...

Deep learning models for electrocardiograms are susceptible to adversarial attack.

Electrocardiogram (ECG) acquisition is increasingly widespread in medical and commercial devices, necessitating the development of automated interpret...

Usefulness of Machine Learning-Based Detection and Classification of Cardiac Arrhythmias With 12-Lead Electrocardiograms.

BACKGROUND: Deep-learning algorithms to annotate electrocardiograms (ECGs) and classify different types of cardiac arrhythmias with the use of a singl...

Machine learning for predicting cardiac events: what does the future hold?

: With the increase in the number of patients with cardiovascular diseases, better risk-prediction models for cardiovascular events are needed. Statis...

Computer Aided Diagnosis for atrial fibrillation based on new artificial adaptive systems.

BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice, having been recognized as a true cardio...

An Integrated Robotic System for MRI-Guided Neuroablation: Preclinical Evaluation.

OBJECTIVE: Treatment of brain tumors requires high precision in order to ensure sufficient treatment while minimizing damage to surrounding healthy ti...

A Review of Algorithm & Hardware Design for AI-Based Biomedical Applications.

This paper reviews the state of the arts and trends of the AI-Based biomedical processing algorithms and hardware. The algorithms and hardware for dif...

ECG Authentication Hardware Design With Low-Power Signal Processing and Neural Network Optimization With Low Precision and Structured Compression.

Biometrics such as facial features, fingerprint, and iris are being used increasingly in modern authentication systems. These methods are now popular ...

Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis.

BACKGROUND: Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and...

Simultaneous multiple features tracking of beats: A representation learning approach to reduce false alarm rates in ICUs.

The high rate of false alarms is a key challenge related to patient care in intensive care units (ICUs) that can result in delayed responses of the me...

End-to-end trained encoder-decoder convolutional neural network for fetal electrocardiogram signal denoising.

OBJECTIVE: Non-invasive fetal electrocardiography has the potential to provide vital information for evaluating the health status of the fetus. Howeve...

Learning metal artifact reduction in cardiac CT images with moving pacemakers.

Metal objects in the human heart such as implanted pacemakers frequently lead to heavy artifacts in reconstructed CT image volumes. Due to cardiac mot...

Machine learning detection of Atrial Fibrillation using wearable technology.

BACKGROUND: Atrial Fibrillation is the most common arrhythmia worldwide with a global age adjusted prevalence of 0.5% in 2010. Anticoagulation treatme...

Nodular Thyroid Disease in the Era of Precision Medicine.

Management of thyroid nodules in the era of precision medicine is continuously changing. Neck ultrasound plays a pivotal role in the diagnosis and sev...

Comprehensive electrocardiographic diagnosis based on deep learning.

Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can pr...

A Catheter-Deployable Soft Robotic Inflatable Basket for Enhanced Conformability to the Left Atrium of the Heart.

This paper presents the design, fabrication, and test results for a novel basket catheter that utilizes soft robotic technology, which can conform to ...