Arrhythmias

Mud Ring Optimization Algorithm with Deep Learning Model for Disease Diagnosis on ECG Monitoring System.

Due to the tremendous growth of the Internet of Things (IoT), sensing technologies, and wearables, the quality of medical services has been enhanced, and it has shifted from standard medical-based health services to real time. Commonly, the sensors can be combined as numerous clinical devices to store the biosignals generated by the physiological actions of the human body. Meanwhile, a familiar me...

Minimally invasive nephron-sparing treatments for T1 renal cell cancer in patients over 75 years: a comparison of outcomes after robot-assisted partial nephrectomy and percutaneous ablation.

PURPOSE: To compare the oncological and perioperative outcomes of robot-assisted partial nephrectomy (RPN) and percutaneous thermal ablation (PTA) for treatment of T1 renal cell cancer (RCC) in patients older than 75 years.

A Novel ECG-Based Deep Learning Algorithm to Predict Cardiomyopathy in Patients With Premature Ventricular Complexes.

BACKGROUND: Premature ventricular complexes (PVCs) are prevalent and, although often benign, they may lead to PVC-induced cardiomyopathy. We created a...

A framework for comparative study of databases and computational methods for arrhythmia detection from single-lead ECG.

Arrhythmia detection from ECG is an important area of computational ECG analysis. However, although a large number of public ECG recordings are availa...

A 0.99-to-4.38 uJ/class Event-Driven Hybrid Neural Network Processor for Full-Spectrum Neural Signal Analyses.

Versatile and energy-efficient neural signal processors are in high demand in brain-machine interfaces and closed-loop neuromodulation applications. I...

Transperineal laser ablation of the prostate as a treatment for benign prostatic hyperplasia and prostate cancer: The results of a Delphi consensus project.

OBJECTIVE: To evaluate transperineal laser ablation (TPLA) with Echolaser® (Echolaser® TPLA, Elesta S.p.A., Calenzano, Italy) as a treatment for benig...

Beat-wise segmentation of electrocardiogram using adaptive windowing and deep neural network.

Timely detection of anomalies and automatic interpretation of an electrocardiogram (ECG) play a crucial role in many healthcare applications, such as ...

CS-based multi-task learning network for arrhythmia reconstruction and classification using ECG signals.

. Although deep learning-based current methods have achieved impressive results in electrocardiograph (ECG) arrhythmia classification issues, they rel...

Detection of preceding sleep apnea using ECG spectrogram during CPAP titration night: A novel machine-learning and bag-of-features framework.

Obstructive sleep apnea (OSA) has a heavy health-related burden on patients and the healthcare system. Continuous positive airway pressure (CPAP) is e...

Deep Learning Models for Stress Analysis in University Students: A Sudoku-Based Study.

Due to the phenomenon of "involution" in China, the current generation of college and university students are experiencing escalating levels of stress...

An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction.

This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that aims to address the problems in arrhythmia diagnosis. The m...

HCformer: Hybrid CNN-Transformer for LDCT Image Denoising.

Low-dose computed tomography (LDCT) is an effective way to reduce radiation exposure for patients. However, it will increase the noise of reconstructe...

Non-invasive localization of the ventricular excitation origin without patient-specific geometries using deep learning.

Cardiovascular diseases account for 17 million deaths per year worldwide. Of these, 25% are categorized as sudden cardiac death, which can be related ...

The use of digital health in heart rhythm care.

INTRODUCTION: Digital health is a broad term that includes telecommunication technologies to collect, share and manipulate health information to impro...

A neuromorphic physiological signal processing system based on VO memristor for next-generation human-machine interface.

Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and heal...

Leveling Up: A Review of Machine Learning Models in the Cardiac ICU.

Machine learning has emerged as a significant tool to augment the medical decision-making process. Studies have steadily accrued detailing algorithms ...

On Merging Feature Engineering and Deep Learning for Diagnosis, Risk Prediction and Age Estimation Based on the 12-Lead ECG.

OBJECTIVE: Over the past few years, deep learning (DL) has been used extensively in research for 12-lead electrocardiogram (ECG) analysis. However, it...

A Product Fuzzy Convolutional Network for Detecting Driving Fatigue.

Existing driving fatigue detection methods rarely consider how to effectively fuse the advantages of the electroencephalogram (EEG) and electrocardiog...

Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease.

BACKGROUND: Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (...

Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms.

BACKGROUND: Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythm...