Arrhythmias

An Energy-Efficient ECG Processor With Ultra-Low-Parameter Multistage Neural Network and Optimized Power-of-Two Quantization.

This work presents an energy-efficient ECG processor designed for Cardiac Arrhythmia Classification. The processor integrates a pre-processing and neural network accelerator, achieved through algorithm-hardware co-design to optimize hardware resources. We propose a lightweight two-stage neural network architecture, where the first stage includes discrete wavelet transform and an ultra-low-paramete...

Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation.

Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological disorders and is the most common heart arrhythmia worldwide, affecting 2 % of the European population. This prevalence increases with age, imposing significant financial, economic, and human burdens. In Europe, stroke is the second leading cause of death and the primary cause of disability, with numbers...

Design and validation of Withings ECG Software 2, a tiny neural network based algorithm for detection of atrial fibrillation.

BACKGROUND: Atrial Fibrillation (AF) is the most common form of arrhythmia in the world with a prevalence of 1%-2%. AF is also associated with an incr...

Time-frequency transformation integrated with a lightweight convolutional neural network for detection of myocardial infarction.

Myocardial infarction (MI) is a life-threatening medical condition that necessitates both timely and precise diagnosis. The enhancement of automated m...

Universal representations in cardiovascular ECG assessment: A self-supervised learning approach.

BACKGROUND: The 12-lead electrocardiogram (ECG) is an established modality for cardiovascular assessment. While deep learning algorithms have shown pr...

An ECG-based machine-learning approach for mortality risk assessment in a large European population.

AIMS: Through a simple machine learning approach, we aimed to assess the risk of all-cause mortality after 5 years in a European population, based on ...

Long-duration electrocardiogram classification based on Subspace Search VMD and Fourier Pooling Broad Learning System.

Detecting early stages of cardiovascular disease from short-duration Electrocardiogram (ECG) signals is challenging. However, long-duration ECG data a...

Predicting Survival and Recurrence of Lung Ablation Patients Using Deep Learning-Based Automatic Segmentation and Radiomics Analysis.

PURPOSE: To predict survival and tumor recurrence following image-guided thermal ablation (IGTA) of lung tumors segmented using a deep learning approa...

A Multicenter Evaluation of the Impact of Therapies on Deep Learning-Based Electrocardiographic Hypertrophic Cardiomyopathy Markers.

Artificial intelligence-enhanced electrocardiography (AI-ECG) can identify hypertrophic cardiomyopathy (HCM) on 12-lead ECGs and offers a novel way to...

Machine Learning-Based Prediction of Death and Hospitalization in Patients With Implantable Cardioverter Defibrillators.

BACKGROUND: Predicting the clinical trajectory of individual patients with implantable cardioverter-defibrillators (ICDs) is essential to inform clini...

Foot fractures diagnosis using a deep convolutional neural network optimized by extreme learning machine and enhanced snow ablation optimizer.

The current investigation proposes a novel hybrid methodology for the diagnosis of the foot fractures. The method uses a combination of deep learning ...

Prediction and Elimination of Physiological Tremor During Control of Teleoperated Robot Based on Deep Learning.

Currently, teleoperated robots, with the operator's input, can fully perceive unknown factors in a complex environment and have strong environmental i...

Assessing operator stress in collaborative robotics: A multimodal approach.

In the era of Industry 4.0, the study of Human-Robot Collaboration (HRC) in advancing modern manufacturing and automation is paramount. An operator ap...

Exploring ChatGPT's potential in ECG interpretation and outcome prediction in emergency department.

BACKGROUND: Approximately 20 % of emergency department (ED) visits involve cardiovascular symptoms. While ECGs are crucial for diagnosing serious cond...

Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.

BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands o...

FlexPoints: Efficient electrocardiogram signal compression for machine learning.

The electrocardiogram (ECG) stands out as one of the most frequently used medical tests, playing a crucial role in the accurate diagnosis and treatmen...

A Multi-Class ECG Signal Classifier Using a Binarized Depthwise Separable CNN with the Merged Convolution-Pooling Method.

Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power cardiac arrhythmia classifiers owing to their hig...

Automated arrhythmia classification based on a pyramid dense connectivity layer and BiLSTM.

BackgroundDeep neural networks (DNNs) have recently been significantly applied to automatic arrhythmia classification. However, their classification a...

Machine learning for improved medical device management: A focus on defibrillator performance.

BackgroundPoorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clin...

Deep learning automatically distinguishes myocarditis patients from normal subjects based on MRI.

Myocarditis, characterized by inflammation of the myocardial tissue, presents substantial risks to cardiovascular functionality, potentially precipita...